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Exam Code: DA-100 Practice exam 2022 by team
DA-100 Analyzing Data with Microsoft Power BI

Exam Number : DA-100
Exam Name : Analyzing Data with Microsoft Power BI

This course will discuss the various methods and best practices that are in line with business and technical requirements for modeling, visualizing, and analyzing data with Power BI. The course will also show how to access and process data from a range of data sources including both relational and non-relational data. This course will also explore how to implement proper security standards and policies across the Power BI spectrum including datasets and groups. The course will also discuss how to manage and deploy reports and dashboards for sharing and content distribution. Finally, this course will show how to build paginated reports within the Power BI service and publish them to a workspace for inclusion within Power BI.

Skills Gained
- Ingest, clean, and transform data
- Model data for performance and scalability
- Design and create reports for data analysis
- Apply and perform advanced report analytics
- Manage and share report assets
- Create paginated reports in Power BI

- Understanding core data concepts.
- Knowledge of working with relational data in the cloud.
- Knowledge of working with non-relational data in the cloud.
- Knowledge of data analysis and visualization concepts.

Course outline
Module 1: Get Started with Microsoft Data Analytics
This module explores the different roles in the data space, outlines the important roles and responsibilities of a Data Analysts, and then explores the landscape of the Power BI portfolio.

Data Analytics and Microsoft
Getting Started with Power BI
Lab : Getting Started
Getting Started
After completing this module, you will be able to:

Explore the different roles in data
Identify the tasks that are performed by a data analyst
Describe the Power BI landscape of products and services
Use the Power BI service
Module 2: Prepare Data in Power BI
This module explores identifying and retrieving data from various data sources. You will also learn the options for connectivity and data storage, and understand the difference and performance implications of connecting directly to data vs. importing it.
Get data from various data sources
Optimize performance
Resolve data errors
Lab : Preparing Data in Power BI Desktop
Prepare Data

After completing this module, you will be able to:

Identify and retrieve data from different data sources
Understand the connection methods and their performance implications
Optimize query performance
Resolve data import errors
Module 3: Clean, Transform, and Load Data in Power BI
This module teaches you the process of profiling and understanding the condition of the data. They will learn how to identify anomalies, look at the size and shape of their data, and perform the proper data cleaning and transforming steps to prepare the data for loading into the model.

Data shaping
Enhance the data structure
Data Profiling
Lab : Transforming and Loading Data
Loading Data

After completing this module, students will be able to:

Apply data shape transformations
Enhance the structure of the data
Profile and examine the data
Module 4: Design a Data Model in Power BI
This module teaches the fundamental concepts of designing and developing a data model for proper performance and scalability. This module will also help you understand and tackle many of the common data modeling issues, including relationships, security, and performance.

Introduction to data modeling
Working with tables
Dimensions and Hierarchies
Lab : Data Modeling in Power BI Desktop
Create Model Relationships
Configure Tables
Review the model interface
Create Quick Measures
Lab : Advanced Data Modeling in Power BI Desktop
Configure many-to-many relationships
Enforce row-level security

After completing this module, you will be able to:

Understand the basics of data modeling
Define relationships and their cardinality
Implement Dimensions and Hierarchies
Create histograms and rankings
Module 5: Create Model Calculations using DAX in Power BI
This module introduces you to the world of DAX and its true power for enhancing a model. You will learn about aggregations and the concepts of Measures, calculated columns and tables, and Time Intelligence functions to solve calculation and data analysis problems.

Introduction to DAX
DAX context
Advanced DAX
Lab : Introduction to DAX in Power BI Desktop
Create calculated tables
Create calculated columns
Create measures
Lab : Advanced DAX in Power BI Desktop
Use the CALCULATE() function to manipulate filter context
use Time Intelligence functions

After completing this module, you will be able to:

Understand DAX
Use DAX for simple formulas and expressions
Create calculated tables and measures
Build simple measures
Work with Time Intelligence and Key Performance Indicators
Module 6: Optimize Model Performance
In this module you are introduced to steps, processes, concepts, and data modeling best practices necessary to optimize a data model for enterprise-level performance.

Optimze the model for performance
Optimize DirectQuery Models
Create and manage Aggregations

After completing this module, you will be able to:

Understand the importance of variables
Enhance the data model
Optimize the storage model
Implement aggregations
Module 7: Create Reports
This module introduces you to the fundamental concepts and principles of designing and building a report, including selecting the correct visuals, designing a page layout, and applying basic but critical functionality. The important course of designing for accessibility is also covered.

Design a report
Enhance the report
Lab : Designing a report in Power BI
Create a live connection in Power BI Desktop
Design a report
Configure visual fields adn format properties
Lab : Enhancing Power BI reports with interaction and formatting
Create and configure Sync Slicers
Create a drillthrough page
Apply conditional formatting
Create and use Bookmarks

After completing this module, you will be able to:

Design a report page layout
Select and add effective visualizations
Add basic report functionality
Add report navigation and interactions
Improve report performance
Design for accessibility
Module 8: Create Dashboards
In this module you will learn how to tell a compelling story through the use of dashboards and the different navigation tools available to provide navigation. You will be introduced to features and functionality and how to enhance dashboards for usability and insights.

Create a Dashboard
Real-time Dashboards
Enhance a Dashboard
Lab : Designing a report in Power BI Desktop - Part 1
Create a Dashboard
Pin visuals to a Dashboard
Configure a Dashboard tile alert
Use Q&A to create a dashboard tile

After completing this module, students will be able to:

Create a Dashboard
Understand real-time Dashboards
Enhance Dashboard usability
Module 9: Create Paginated Reports in Power BI
This module will teach you about paginated reports, including what they are how they fit into Power BI. You will then learn how to build and publish a report.

Paginated report overview
Create Paginated reports
Lab : Creating a Paginated report
Use Power BI Report Builder
Design a multi-page report layout
Define a data source
Define a dataset
Create a report parameter
Export a report to PDF

After completing this module, you will be able to:

Explain paginated reports
Create a paginated report
Create and configure a data source and dataset
Work with charts and tables
Publish a report
Module 10: Perform Advanced Analytics
This module helps you apply additional features to enhance the report for analytical insights in the data, equipping you with the steps to use the report for actual data analysis. You will also perform advanced analytics using AI visuals on the report for even deeper and meaningful data insights.

Advanced Analytics
Data Insights through AI visuals
Lab : Data Analysis in Power BI Desktop
Create animated scatter charts
Use teh visual to forecast values
Work with Decomposition Tree visual
Work with the Key Influencers visual

After completing this module, you will be able to:

Explore statistical summary
Use the Analyze feature
Identify outliers in data
Conduct time-series analysis
Use the AI visuals
Use the Advanced Analytics custom visual
Module 11: Create and Manage Workspaces
This module will introduce you to Workspaces, including how to create and manage them. You will also learn how to share content, including reports and dashboards, and then learn how to distribute an App.

Creating Workspaces
Sharing and Managing Assets
Lab : Publishing and Sharing Power BI Content
Map security principals to dataset roles
Share a dashboard
Publish an App

After completing this module, you will be able to:

Create and manage a workspace
Understand workspace collaboration
Monitor workspace usage and performance
Distribute an App
Module 12: Manage Datasets in Power BI
In this module you will learn the concepts of managing Power BI assets, including datasets and workspaces. You will also publish datasets to the Power BI service, then refresh and secure them.


After completing this module, you will be able to:

Create and work with parameters
Manage datasets
Configure dataset refresh
Troubleshoot gateway connectivity
Module 13: Row-level security
This module teaches you the steps for implementing and configuring security in Power BI to secure Power BI assets.

Security in Power BI

After completing this module, you will be able to:

Understand the aspects of Power BI security
Configure row-level security roles and group memberships

Analyzing Data with Microsoft Power BI
Microsoft Analyzing learn
Killexams : Microsoft Analyzing learn - BingNews Search results Killexams : Microsoft Analyzing learn - BingNews Killexams : OpenAI Impact Analysis: Microsoft, Google And Nvidia
User dialogue with AI chat bot on futuristic smartphone interface


OpenAI’s latest chatbot, “ChatGPT”, has become talk of the town in recent weeks following the “DALL-E” sensation from just a few months back after Microsoft (NASDAQ:MSFT) launched “Microsoft Designer”.

ChatGPT is powered by the “GPT-3” language model, which was first introduced to the public a few years ago. While GPT-3 has already been deployed across hundreds of applications since its introduction, which enables the generation of “a text completion in natural language” in response to simple text prompts from humans, the latest release of the ChatGPT chatbot’s availability to the public underscores the model’s significant improvements since.

A previous beta of the chatbot, which was only made available to a handful of users for testing purposes, was ladened with limitations, spanning the inability to reject requests that do not make sense or are inappropriate, to a general lack of common sense. While some limitations remain in today’s version of ChatGPT that has been released to the public for trial, it has come a long way with significant improvements to its ability to “answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests”.

The impressive capabilities of ChatGPT today brings into question the viability of near-and-dear systems like Google Search (GOOG, GOOGL) which is intricately linked to our day-to-day personal settings, as well as opportunities to facilitators of high-performance computing (“HPC”) spanning hyperscalers like Microsoft to upstream chipmakers like Nvidia (NVDA). The following analysis will provide an overview of OpenAI’s latest developments when it comes to language models, as well as its longer-term implications on technology bellwethers today including Google, Microsoft and Nvidia.

What Is ChatGPT?

OpenAI is a non-profit AI technology development platform “co-founded by Tesla (TSLA) CEO Elon Musk and [Sam] Altman with other investors”, including Microsoft which invested $1 billion into the company in 2019. The company has released public access to its chatbot, ChatGPT, last week, spurring a slew of AI-generated dialogues and responses spanning simple logic Q&A to well-versed essays and poems that could pass as an “A- grade” college research paper, that have overtaken the internet.

ChatGPT is based on the GPT-3 language model introduced in 2020, which is capable of mimicking human responses to simple text prompts. GPT-3 is a so-called “pre-trained” model that leverages an existing set of trained and fine-tuned data to make inferences via AI/ML. This generally addresses three main limitations previously identified in predecessor language models:

  • Practicality: It eliminates the need for large volumes of data that would be costly to label before being used in training the language model. By using a pre-trained model, GPT-3 can generate adequate responses by using “only a few labelled samples”, thus enabling greater cost- and time-efficiencies in development.
  • Elimination of “overfitting” and overly specific responses: Training a model with large volumes of data risks “overfitting”, or too much data that instead confuses a model from performing accurately. Alternatively, training a model with large volumes of data could also eliminate its ability to “generalize” beyond a specific domain, thus limiting its performance capacity.

When machine learning algorithms are constructed, they leverage a demo dataset to train the model. However, when the model trains for too long on demo data or when the model is too complex, it can start to learn the “noise,” or irrelevant information, within the dataset. When the model memorizes the noise and fits too closely to the training set, the model becomes “overfitted,” and it is unable to generalize well to new data.

Source: IBM

  • Enables dialogue via simple prompts: Pre-trained models like GPT-3 also do “not require large supervised data sets to learn most language tasks”, mimicking human responses to typically brief directives.

Consisting of 175 billion parameters, GPT-3 is more than 100x larger than its predecessor, “GPT-2”, which consists of only 1.5 billion parameters, and 10x larger than Microsoft’s “Turing NLG” language model introduced in 2020, which consists of 17 billion parameters. This suggests greater performance and applicability by GPT-3, which is further corroborated by its ability to outperform “fine-tuned state-of-the-art algorithms” (“SOTA”) spanning other natural language processing (“NLP”) systems, speech recognition and recommendation systems. With 175 billion parameters, GPT-3 can achieve response accuracy of more than 80% in a “few-shots” setting.

Few-Shot Learning

"Larger models make increasingly efficient use of in-context information" (Language Models are Few-Shot Learners)

Few-shot learning essentially enables a pre-trained language model like GPT-3 to “generalize over new categories of data by using only a few labelled samples per class”, and is a “paradigm of meta-learning” or “learning-to-learn”:

One potential route addressing [limitations of NLP systems] is meta-learning, which in the context of language models means the model develops a broad set of skills and pattern recognition abilities at training time, and then uses those abilities at inference time to rapidly adapt to or recognize the desired task. In-context learning uses the text input of a pretrained language model as a form of tasks specification. The model is conditioned on a natural language instruction and/or a few demonstrations of the task and is then expected to complete further instances of the tasks simply by predicting what comes next.

Source: “Language Models are Few-Shot Learners

As mentioned in the earlier section, ChatGPT has improved significantly from the GPT-3 API closed beta launched in 2020, which only few had access to. Just merely two years ago, the language model did not know how to reject nonsense questions or say “I don’t know”:


Example of GPT-3 API Inefficiencies at Close Beta Phase (

Fast-forward to today, not only can the model reject nonsense questions and inappropriate requests, ChatGPT can also make suggestions spanning simple one-liner responses to prose-form essays:


ChatGPT vs. InstructGPT (OpenAI)

Yet, limitations remain, nonetheless, including the chatbot’s rare regurgitation of repeated words in list answers, ability to bypass rules programmed to prevent discussion on illegal activities, and provision of incorrect and/or inaccurate responses.

A Threat To Google?

The capabilities of ChatGPT underscores its potential in becoming a threat to Google’s search engine, which is currently the biggest ad distribution channel and revenue driver for the company. With ChatGPT recently becoming an open platform available to the public for free trial, many are realizing that the chatbot is not only capable of generating fairly accurate search results and/or answering fact-based Q&A requests, but also in-depth analyses and suggestions. ChatGPT is essentially a take-home homework buddy for some, given its capability of generating quality work like “take-home 1,000 word undergraduate essays” (which, let’s be honest, primarily rely on research scoured through Google Search) that outperforms those of an average student’s.

These capabilities are sufficient to put Google on notice, as they put the Search leader at risk of becoming obsolete. OpenAI’s GPT-3 model essentially addresses the call from more than 40% of corporate employees across the U.S. today for low-code techniques critical in creating value in the data-driven era, while potentially eliminating the need for Google Search altogether if commercialized.

But Google has not completely missed the beat when it comes to AI developments. As we had discussed in a previous coverage, Google is currently working on its own “Language Model of Dialogue Applications”, or LaMDA 2 language model (if you’ve been following the controversial discussion on Google’s allegedly sentient chatbot a few months back, that was LaMDA).

“Language Model of Dialogue Applications”, or “LaMDA”, was also unveiled at this year’s I/O event. LaMDA is trained to engage in conversation and dialogue to help Google better understand the “intent of search queries”. While LaMDA remains in research phase, the ultimate integration of the breakthrough technology into Google Search will not only make the search engine more user-friendly, but also enable search results with greater accuracy.

Source: “Where Will Google Stock Be In 10 Years?

AI-enabled language model competencies needs to be a key focus for Google if it wants to maintain its leadership in online search engines over the longer-term. And the company simply recognizes that. In addition to LaMDA, Google has also been working on a “Multitask Unified Model” (“MUM”), which would refine online searches by allowing users to combine audio, text and image prompts into one single search. But nonetheless, OpenAI’s GPT-3 remains a threat given LaMDA features only 137 billion parameters, which is still a wide distance from GPT-3’s 175 billion parameters that essentially generates higher accuracy. And the latest controversy on whether LaMDA is sentient has likely been a setback to its development, putting OpenAI’s ChatGPT potentially a step ahead.

Google Search currently dominates the online search engine market with close to 70% share of everyday online queries made worldwide. Ironically, Google is also the most-searched term on rival search engine Bing, which illustrates how critical of a role it plays in our everyday life settings. It is also one of the biggest and most effective online ad distribution channels today, and accounts for almost 60% of Alphabet’s consolidated quarterly revenues over the past 12 months.

But OpenAI could easily disrupt this current norm, and potentially deliver its backer Microsoft a leg-up, even if it is not directly through Bing (we already see Microsoft leveraging OpenAI’s image-generating capabilities in its latest foray in the burgeoning low-code design industry). The alternative is for Google to keep up with its significant investments into both its cloud-computing capabilities, as well as on training its AI models to both Excellerate the overall competitiveness of Search, and capitalize on growing HPC capabilities stemming from an expanding AI addressable market in the years ahead. While this means potential margin compression in the near-term, it would be critical to sustaining its long-term growth trajectory.

Microsoft’s Prescient Investment

As mentioned in the earlier section, Microsoft is an early investor in OpenAI. With the company’s technologies now coming into fruition, Microsoft has inadvertently become a key beneficiary.

Prior to ChatGPT’s latest deployment for public free trial and feedback solicitation, OpenAI was already making noise across the internet a few months ago with DALL-E 2. DALL-E 2 essentially uses AI to convert simple text prompts into AI-generated images in all sorts of combinations by leveraging what is already available online, and is crucial in materializing on low-code graphic designing capabilities to users. Microsoft became the latest to leverage DALL-E 2 in its latest Microsoft Designer app, which will be key to its latest foray in low-code design capabilities against industry leaders Adobe (ADBE) and Canva. With capabilities of OpenAI’s DALL-E 2, Microsoft is ready to compete for a share of the growing pie in low- and no-code design that is set to exceed $60 billion by 2024, underscoring significant return potential on that front from its prescient decision to invest $1 billion into OpenAI just three years ago.

And now with GPT-3 and the latest development on ChatGPT, Microsoft return potential on its early investment in OpenAI has just gotten better. First, ChatGPT and the improved GPT-3 model on which the chatbot is built are both trained on an “Azure AI supercomputing infrastructure”. This essentially provides validation to the technological competency of Microsoft’s foray in HPC, a $108+ billion addressable market. As we have previously discussed, GPT-3 is not the only SOTA AI algorithm today. Instead, there are many more complex language models, among other AI workloads, that require significant computing power – the GPT-3 alone requires “400 gigabits per second of network connectivity for each GPU server” – underscoring the extent of massive demand for HPC over the coming years.

Second, the eventual commercialization of GPT-3 and ChatGPT could mean integration into Microsoft’s existing product portfolio to further strengthen the software giant’s reach across its respective addressable markets. As discussed in the earlier section, GPT-3 could bolster Bing’s share of the online search engine market over the longer-term, which would inadvertently drive greater digital ad revenues to the platform. Although a farfetched speculation given Bing’s nominal market share today when compared to Google Search’s, any improvements to Microsoft’s search capabilities would be a welcomed sight, nonetheless, and would help chip away at Google’s market leadership and expand the software giant’s share of fast-expanding search ad dollars instead:

Search: Online search engines are currently the most popular digital advertising platforms, boasting 19% y/y growth in the first half of the year. And the trends are expected to extend into the foreseeable future, as search ads approach the end of 2022 with at least 17% y/y growth…And looking forward to 2023, demand for search ads is expected to grow by about 13% y/y, with deceleration consistent with the IMF’s forecast for further economic contraction in the following year.

Source: “Ad-Tech Round-Up: Why We Think Google And Amazon Will Rise On Top

In addition to Bing, Microsoft’s latest dabble in the Metaverse could also benefit from the commercialization of ChatGPT. As we have discussed in detail in a previous coverage on Microsoft's stock, the company has been stepping up on its “ability in capitalizing on growing opportunities stemming from digital transformation needs across the consumer and enterprise sectors” – especially in the post-pandemic era norm of location-agnostic work. This includes Microsoft’s introduction of “Mesh”, its virtual world currently accessible through Microsoft Teams, as well as “Connected Spaces” deployed through Dynamics 365 and “Digital Twins” via Azure. And ChatGPT would be a significant addition to Microsoft’s portfolio of virtual-environment-centric enterprise software by enabling capitalization of opportunities stemming from “digitization of more than 25 million retail and industrial spaces in need of digital customer support and/or smart contactless check-out cashiers” over the longer-term.

Continued commercialization and integration of OpenAI’s technologies would effectively enable greater returns on investment for Microsoft. This can be done directly through the eventual sale of OpenAI products, and indirectly via integration of OpenAI’s technologies into existing Microsoft services to enable deeper reach into customers’ pockets. Although a nominal investment based on Microsoft’s sprawling balance sheet today, OpenAI could become a critical piece to sustaining the tech giant’s “mission critical” role in the provision of enterprise software over the longer-term.

Benefits Flowing Upstream To Nvidia

Upstream chipmakers are a critical backbone of AI-driven innovations. This makes Nvidia a key beneficiary of growing demands from HPC, given its prowess in both AI and graphics processors:

On the enterprise front, GPUs are also in high demand from hyperscale data center and high performance computing (“HPC”) segments considering the technology’s ability in processing complex workloads related to machine learning, deep learning, AI and data mining. And the “Nvidia A100” GPU – one of many data center GPUs offered by the chipmaker – does just that. The technology, introduced in 2020, is built based on the Ampere architecture as discussed above and delivers up to 20x higher performance than its predecessors. The A100 is built specifically for supporting “data analytics, scientific computing and cloud graphics”. There is also the recently introduced “HGX AI Supercomputer” platform built on the Nvidia A100, which is capable of providing “extreme performance to enable HPC innovation”.

The chipmaker’s continued commitment to improving solutions for enterprise workloads makes it well-positioned for capturing growing opportunities from the data center and HPC segments in coming years. Global demand for data center chips is expected to rapidly expand at a compounded annual growth rate (“CAGR”) of 36.7% over the next five years.

Source: “Is Nvidia Stock A Buy On The Dip? Just Look At Its Resilience Without Arm

Nvidia’s latest foray in data center CPUs and CPU+GPU superchips through the “Grace” and “Hopper” architectures also makes it well-positioned for capturing demand stemming from transformer models like GPT-3 which require significant HPC performance:

The supercomputer developed for OpenAI is a single system with more than 285,000 CPU cores, 10,000 GPUs and 400 gigabits per second of network connectivity for each GPU server.

Source: Nvidia

And as the computing performance and cost efficiency of Nvidia’s hardware improves, transformer models like GPT-3 will also become more refined, putting them a step closer to commercialization. The latest research on demand for chips and other essential hardware critical to enabling AI use cases predicts an addressable market of approximately $1.7 trillion by the end of the decade, with improvements to performance and cost-efficiency being key drivers to the opportunity’s continued expansion. And these are two traits that Nvidia continues to deliver on:

Thanks primarily to Nvidia, the performance of AI training accelerators has been advancing at an astounding rate. Compared to the K80 chip that Nvidia released in 2014, the latest accelerator delivers 195x the performance on a total cost of ownership (“TCO”) adjusted basis...TCO measures an AI training system’s unit price and operating costs…As a baseline, Moore’s Law predicts that the number of transistors on a chip doubles every 18 months to two years, [and] historically it has translated into a ~30% annualized decline in costs…AI chip performance has improved at a 93% rate per year since 2014, translating into a cost decline of 48% per year…Measuring the time to train large AI models instead of Moore’s Law, we believe transistor count will become more important as AI hardware chip designs increase in complexity…As the number of modelled parameters and pools of training data has scaled, Nvidia has [also] added more memory to its chips, enabling larger batch sizes when training. The latest generation of ultra-high bandwidth memory technology, HBM2e, is much faster than the GDDR5 memory found in Nvidia’s 2014 K80. With 80 gigabytes of HBM2e memory, Nvidia’s H100 can deliver 6.25x the memory bandwidth of the K80...

Source: ARK Investment Management

With Nvidia not only enabling materialization of language models like GPT-3, but also improving the economics of said transformer models’ deployment in the future, the company is well-poised to benefit from a robust demand environment over coming years from HPC alone. This will not only benefit Nvidia’s higher-margin data center business, but also potentially offset any near-term headwinds stemming from intensifying geopolitical risks, and/or cyclical weakness.

The Bottom Line

Among the three tickers analyzed in association with OpenAI’s latest developments, Microsoft has been most resilient amid this year’s market rout. It is also likely the most well-positioned to benefit from OpenAI's AI technologies. Meanwhile, Google has been punished for waning demand across the inherently macro-sensitive ad sector, and Nvidia caught in the hardest-hit semiconductor industry on fears of a cyclical downturn following a multi-year boom, among other industry-wide challenges like geopolitical risks.


Microsoft’s relative resilience is not unreasonable though. Its provision of “mission-critical” software makes it less prone to recession-driven budget cuts across the board. Although no corner of any industry has been left untouched by the unravelling global economy, demand for back-office software like Microsoft’s Dynamics 365, Office 365, and Power BI have also proven to be more resilient given typically fixed, “long-term contracts, which creates far less noise during times of uncertain macro”. This is further corroborated by Microsoft’s robust results for the September-quarter, despite reasonable warning from management aimed at tempering investors’ expectations ahead of mounting macro uncertainties that bring about demand risks, as well as FX headwinds.

And on a longer-term basis, Microsoft’s continued investment in core innovations capable of expanding its addressable market – whether it is the planned consolidation of Activision Blizzard (ATVI) to bolster its presence in gaming; its existing investment in OpenAI to bolster its search, cloud, and productivity software capabilities as discussed in the foregoing analysis; or continued deployment of capital towards expanding Azure to ensure adequate capitalization of growing opportunities – reinforces the sustainability of its growth trajectory, making it one of the most reasonable investments at current levels among other tickets discussed in today’s analysis.


Meanwhile for Google, the increasing threat of obsolescence of Search – which is where its meat and potatoes are at – risks a more tempered recovery when macroeconomic headwinds subside. This accordingly makes the company’s longer-term growth outlook at risk of greater moderation when compared to the sprawling, yet sustained, growth and market dominance observed today.

While the recent macroeconomic downturn has made Google a compelling investment opportunity for sustained upside potential into the longer-term, said gains might become more moderate than expected over time, especially if its AI and cloud-computing efforts fail to catch up to nascent rivals in the market today.


As for Nvidia, although its valuation has come down significantly while its longer-term growth prospects continue to demonstrate sustainability supported by its “mission critical” role in enabling next-generation innovations like OpenAI’s language model, the stock continues to trade at a premium to peers with similar growth profiles. And this premium, though justifiable by its market leadership in AI and GPU processors, risks increasing the stock’s vulnerability to a further downtrend in tandem with broader-market declines.

While Microsoft also trades at a slight valuation premium to comparable peers, Nvidia faces greater industry- and company-specific risks, including challenges of cyclical weakness in semiconductor demand in the near-term, as well as repercussions of rising geopolitical tension between the U.S. and China. And these could potentially bode unfavourably given today’s risk-off market climate ahead of a protracted monetary policy tightening trajectory, which potentially poses better entry opportunities for the stock over the coming months instead of in the immediate-term.

Mon, 05 Dec 2022 05:15:00 -0600 en text/html
Killexams : SOC Turns to Homegrown Machine Learning to Catch Cyber-Intruders

Using an internally developed machine learning model trained on log data, the information security team for a French bank found it could detect three new types of data exfiltration that rules-based security appliances did not catch.

Carole Boijaud, a cybersecurity engineer with Credit Agricole Group Infrastructure Platform (CA-GIP), will take the stage at next week's Black Hat Europe 2022 conference to detail the research into the technique, in a session entitled, "Thresholds Are for Old Threats: Demystifying AI and Machine Learning to Enhance SOC Detection." The team took daily summary data from log files, extracted interesting features from the data, and used that to find anomalies in the bank's Web traffic. 

The research focused on how to better detect data exfiltration by attackers, and resulted in identification of attacks that the company's previous system failed to detect, she says.

"We implemented our own simulation of threats, of what we wanted to see, so we were able to see what could identify in our own traffic," she says. "When we didn't detect [a specific threat], we tried to figure out what is different, and we tried to understand what was going on."

As machine learning has become a buzzword in the cybersecurity industry, some companies and academic researchers are still making headway in experimenting with their own data to find threats that might otherwise hide in the noise. Microsoft, for example, used data collected from the telemetry of 400,000 customers to identify specific attack groups and, using those classifications, predict future actions of the attackers. Other firms are using machine learning techniques, such as genetic algorithms, to help detect accounts on cloud computing platforms that have too many permissions.

There are a variety of benefits from analyzing your own data with a homegrown system, says Boijaud. Security operation centers (SOCs) gain a better understanding of their network traffic and user activity, and security analysts can gain more insight into the threats attacking their systems. While Credit Agricole has its own platform group to manage infrastructure, handle security, and conduct research, even smaller enterprises can benefit from applying machine learning and data analysis, Boijaud says.

"Developing your own model is not that expensive and I'm convinced that everyone can do it," she says. "If you have access to the data, and you have people who know the logs, they can create their own pipeline, at least in the beginning."

Finding the Right Data Points to Monitor

The cybersecurity engineering team used a data-analysis technique known as clustering to identify the most important features to track in their analysis. Among the features that were deemed most significant included the popularity of domains, the number of times systems reached out to specific domains, and whether the request used an IP address or a standard domain name.

"Based on the representation of the data and the fact that we have been monitoring the daily behavior of the machines, we have been able to identify those features," says Boijaud. "Machine learning is about mathematics and models, but one of the important facts is how you choose to represent the data and that requires understanding the data and that means we need people, like cybersecurity engineers, who understand this field."

After selecting the features that are most significant in classifications, the team used a technique known as "isolation forest" to find the outliers in the data. The isolation forest algorithm organizes data into several logical trees based on their values, and then analyzes the trees to determine the characteristics of outliers. The approach scales easily to handle a large number of features and is relatively light, processing-wise.

The initial efforts resulted in the model learning to detect three types of exfiltration attacks that the company would not otherwise have detected with existing security appliances. Overall, about half the exfiltration attacks could be detected with a low false-positive rate, Boijaud says.

Not All Network Anomalies Are Malicious

The engineers also had to find ways to determine what anomalies indicated malicious attacks and what may be nonhuman — but benign — traffic. Advertising tags and requests sent to third-party tracking servers were also caught by the system, as they tend to match the definitions of anomalies, but could be filtered out of the final results.

Automating the initial analysis of security events can help companies more quickly triage and identify potential attacks. By doing the research themselves, security teams gain additional insight into their data and can more easily determine what is an attack and what may be benign, Boijaud says.

CA-GIP plans to expand the analysis approach to use cases beyond detecting exfiltration using Web attacks, she says.

Fri, 02 Dec 2022 01:43:00 -0600 en text/html
Killexams : Calculating The Intrinsic Value Of Microsoft Corporation (NASDAQ:MSFT)

Today we will run through one way of estimating the intrinsic value of Microsoft Corporation (NASDAQ:MSFT) by taking the expected future cash flows and discounting them to today's value. One way to achieve this is by employing the Discounted Cash Flow (DCF) model. Models like these may appear beyond the comprehension of a lay person, but they're fairly easy to follow.

Companies can be valued in a lot of ways, so we would point out that a DCF is not perfect for every situation. Anyone interested in learning a bit more about intrinsic value should have a read of the Simply Wall St analysis model.

See our latest analysis for Microsoft

Step By Step Through The Calculation

We are going to use a two-stage DCF model, which, as the name states, takes into account two stages of growth. The first stage is generally a higher growth period which levels off heading towards the terminal value, captured in the second 'steady growth' period. In the first stage we need to estimate the cash flows to the business over the next ten years. Where possible we use analyst estimates, but when these aren't available we extrapolate the previous free cash flow (FCF) from the last estimate or reported value. We assume companies with shrinking free cash flow will slow their rate of shrinkage, and that companies with growing free cash flow will see their growth rate slow, over this period. We do this to reflect that growth tends to slow more in the early years than it does in later years.

Generally we assume that a dollar today is more valuable than a dollar in the future, and so the sum of these future cash flows is then discounted to today's value:

10-year free cash flow (FCF) forecast











Levered FCF ($, Millions)











Growth Rate Estimate Source

Analyst x23

Analyst x22

Analyst x10

Analyst x6

Analyst x4

Est @ 7.98%

Est @ 6.18%

Est @ 4.92%

Est @ 4.04%

Est @ 3.42%

Present Value ($, Millions) Discounted @ 7.6%











("Est" = FCF growth rate estimated by Simply Wall St)
Present Value of 10-year Cash Flow (PVCF) = US$750b

After calculating the present value of future cash flows in the initial 10-year period, we need to calculate the Terminal Value, which accounts for all future cash flows beyond the first stage. The Gordon Growth formula is used to calculate Terminal Value at a future annual growth rate equal to the 5-year average of the 10-year government bond yield of 2.0%. We discount the terminal cash flows to today's value at a cost of equity of 7.6%.

Terminal Value (TV)= FCF2032 × (1 + g) ÷ (r – g) = US$151b× (1 + 2.0%) ÷ (7.6%– 2.0%) = US$2.7t

Present Value of Terminal Value (PVTV)= TV / (1 + r)10= US$2.7t÷ ( 1 + 7.6%)10= US$1.3t

The total value is the sum of cash flows for the next ten years plus the discounted terminal value, which results in the Total Equity Value, which in this case is US$2.1t. In the final step we divide the equity value by the number of shares outstanding. Relative to the current share price of US$245, the company appears about fair value at a 12% discount to where the stock price trades currently. Remember though, that this is just an approximate valuation, and like any complex formula - garbage in, garbage out.


The Assumptions

The calculation above is very dependent on two assumptions. The first is the discount rate and the other is the cash flows. You don't have to agree with these inputs, I recommend redoing the calculations yourself and playing with them. The DCF also does not consider the possible cyclicality of an industry, or a company's future capital requirements, so it does not deliver a full picture of a company's potential performance. Given that we are looking at Microsoft as potential shareholders, the cost of equity is used as the discount rate, rather than the cost of capital (or weighted average cost of capital, WACC) which accounts for debt. In this calculation we've used 7.6%, which is based on a levered beta of 1.008. Beta is a measure of a stock's volatility, compared to the market as a whole. We get our beta from the industry average beta of globally comparable companies, with an imposed limit between 0.8 and 2.0, which is a reasonable range for a stable business.

Next Steps:

Valuation is only one side of the coin in terms of building your investment thesis, and it is only one of many factors that you need to assess for a company. It's not possible to obtain a foolproof valuation with a DCF model. Instead the best use for a DCF model is to test certain assumptions and theories to see if they would lead to the company being undervalued or overvalued. If a company grows at a different rate, or if its cost of equity or risk free rate changes sharply, the output can look very different. For Microsoft, there are three fundamental items you should look at:

  1. Risks: Be aware that Microsoft is showing 1 warning sign in our investment analysis , you should know about...

  2. Future Earnings: How does MSFT's growth rate compare to its peers and the wider market? Dig deeper into the analyst consensus number for the upcoming years by interacting with our free analyst growth expectation chart.

  3. Other Solid Businesses: Low debt, high returns on equity and good past performance are fundamental to a strong business. Why not explore our interactive list of stocks with solid business fundamentals to see if there are other companies you may not have considered!

PS. The Simply Wall St app conducts a discounted cash flow valuation for every stock on the NASDAQGS every day. If you want to find the calculation for other stocks just search here.

Have feedback on this article? Concerned about the content? Get in touch with us directly. Alternatively, email editorial-team (at)

This article by Simply Wall St is general in nature. We provide commentary based on historical data and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. We aim to bring you long-term focused analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material. Simply Wall St has no position in any stocks mentioned.

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Wed, 07 Dec 2022 00:00:00 -0600 en-US text/html
Killexams : Where CISOs rely on AI and machine learning to strengthen cybersecurity

Check out all the on-demand sessions from the Intelligent Security Summit here.

Faced with an onslaught of malware-less attacks that are increasingly hard to identify and stop, CISOs are contending with a threatscape where bad actors innovate faster than security and IT teams can keep up. However, artificial intelligence (AI) and machine learning (ML) are proving effective in strengthening cybersecurity by scaling data analysis volume while increasing response speeds and securing digital transformation projects under construction. 

“AI is incredibly, incredibly effective in processing large amounts of data and classifying this data to determine what is good and what’s bad. At Microsoft, we process 24 trillion signals every single day, and that’s across identities and endpoints and devices and collaboration tools, and much more. And without AI, we simply could not tackle this,” Vasu Jakkal, corporate vice president for Microsoft security, compliance, identity, and privacy, told her keynotes’ audience at the RSA Conference earlier this year.

AI helps close skills gaps, growing the market  

2022 is a breakout year for AI and ML in cybersecurity. Both technologies enable cybersecurity and IT teams to Excellerate the insights, productivity and economies of scale they can achieve with smaller teams. 93% of IT executives are already using or considering implementing AI and ML to strengthen their cybersecurity tech stacks. Of those, 64% of IT executives have implemented AI for security in at least one of their security life cycle processes, and 29% are evaluating vendors. 

CISOs tell VentureBeat that one of the primary factors driving adoption is the need to get more revenue-related projects done with fewer people. In addition, AI and ML-based apps and platforms are helping solve the cybersecurity skills shortages that put organizations at a higher risk of breaches. According to the (ISC)² Cybersecurity Workforce Study, “3.4 million more cybersecurity workers are needed to secure assets effectively.”


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CISOs also need the real-time data insights that AI- and ML-based systems provide to fine-tune predictive models, gain a holistic view of their networks and continue implementing their zero-trust security framework and strategy. As a result, enterprise spending on AI- and ML-based cybersecurity solutions are projected to attain a 24% compound annual growth rate (CAGR) through 2027 and reach a market value of $46 billion.

AI’s leading use cases in cybersecurity 

It’s common to find enterprises not tracking up to 40% of their endpoints, making it more challenging because many IT teams aren’t sure how many endpoints their internal processes are creating in a given year. Over a third, or 35%, of enterprises using AI today to strengthen their tech stacks say that endpoint discovery and asset management is their leading use case. Enterprises plan to increase their use of endpoint discovery and asset management by 15% in three years, eventually installed in nearly half of all enterprises. 

It’s understandable why endpoint recovery and asset management are highly prioritized due to how loosely managed their digital certificates are. For example, Keyfactor found that 40% of enterprises use spreadsheets to track digital certificates manually, and 57% do not have an accurate inventory of SSH keys. 

Additional use cases revolve around cybersecurity investments related to zero-trust initiatives, including vulnerability and patch management, access management and identity access management (IAM). For example, 34% of enterprises are using AI-based vulnerability and patch management systems today, which is expected to jump to over 40% in three years. 

Improving endpoint discovery and asset management along with patch management continue to lead CISOs’ priorities this year. Source: AI and automation for cybersecurity report, IBM Institute for Business Value | Benchmark Insights, 2022.

Who CISOs trust to get it right 

Over 11,700 companies in Crunchbase are affiliated with cybersecurity, with over 1,200 mentioning AI and ML as core tech stacks and products and service strategies. As a result, there’s an abundance of cybersecurity vendors to consider, and over a thousand can use AL, ML or both to solve security problems.

CISOs look to AI and ML cybersecurity vendors who can most help consolidate their tech stacks first. They’re also looking for AI and ML applications, systems and platforms that deliver measurable business value while being feasible to implement, given their organizations’ limited resources. CISOs are getting quick wins using this approach. 

The most common use cases are AI- and ML-based cybersecurity implementations of transaction-fraud detection, file-based malware detection, process behavior analysis, and web domain and reputation assessment. CISOs want AI and Ml systems that can identify false positives and differentiate between attackers and admins. That’s because they meet the requirement of securing threat vectors while delivering operational efficiency and being technically feasible. 

VentureBeat’s conversations with CISOs at industry events, including RSA, BlackHat 2022, CrowdStrike’s Fal.Con and others, found several core areas where AI and ML adoption continue to get funded despite budget pressures being felt across IT and security teams. These areas include behavioral analytics (now a core part of many cybersecurity platforms), bot-based patch management, compliance, identity access management (IAM), identifying and securing machine identities, and privileged access management (PAM), where AI is used for scoring risk and validating identities. 

In addition, the following are areas where AI and ML are delivering value to enterprises today:

Using AL and ML to Excellerate behavioral analytics, improving authentication accuracy. Endpoint protection platform (EPP), endpoint detection and response (EDR) unified endpoint management (UEM), and a few public cloud providers, including Amazon AWS, Microsoft Azure, and others, are combining AI techniques and ML models to Excellerate security personalization while enforcing least-privileged access. Leading cybersecurity providers are integrating predictive AI and ML to adapt security policies and roles to each user in real time based on the patterns of where and when they attempt to log in, their device type, device configuration and several other classes of variables. 

Leading providers include Blackberry Persona, Broadcom, CrowdStrike, CyberArk, Cybereason, Ivanti, SentinelOne, Microsoft, McAfee, Sophos, VMware Carbon Black and others. Enterprises say this approach to using AI-based endpoint management decreases the risk of lost or stolen devices, protecting against device and app cloning and user impersonation.

Microsoft Defender’s unique approach of combining AI and ML techniques to Excellerate behavioral blocking and containment has proven effective in identifying and stopping breach attempts based on an analysis of previous behaviors combined with learned insights from pre- and post-execution sensors. Source: Microsoft 365 Defender Portal pages, 2022, Microsoft 365 Docs.

Discovering and securing endpoints by combining ML and natural language processing (NLP). Attack surface management (ASM) is comprised of external attack surface management (EASM), cyberasset attack surface management (CAASM), and digital risk protection services (DRPS), according to Gartner’s 2022 Innovation Insight for Attack Surface Management report (preprint courtesy of Palo Alto Networks). Gartner predicts that by 2026, 20% of companies will have more than 95% visibility of all their assets, which will be prioritized by risk and control coverage by implementing CAASM functionality, up from less than 1% in 2022. 

Leading vendors in this area are combining ML algorithms and NLP techniques to discover, map and define endpoint security plans to protect every endpoint in an organization. Leading vendors include Axonius, Brinqa, Cyberpion, CyCognito, FireCompass, JupiterOne, LookingGlass Cyber, Noetic Cyber, Palo Alto Networks (via its acquisition of Expanse), Randori and others. 

Using AI and ML to automate indicators of attack (IOAs), thwarting intrusion and breach attempts. AI-based IOAs fortify existing defenses using cloud-based ML and real-time threat intelligence to analyze events at runtime and dynamically issue IOAs to the sensor. The sensor then correlates the AI-generated IOAs (behavioral event data) with local events and file data to assess maliciousness. CrowdStrike says AI-powered IOAs operate asynchronously alongside existing layers of sensor defense, including sensor-based ML and existing IOAs. Its AI-based IOAs combine cloud-native ML and human expertise on a common platform invented by the company more than a decade ago. Since their introduction, AI-based IOAs have proven effective in identifying and thwarting intrusion and breach attempts while defeating them in real time based on actual adversary behavior. 

AI-powered IOAs rely on cloud-native ML models trained using telemetry data from CrowdStrike Security Cloud combined with expertise from the company’s threat-hunting teams. IOAs are analyzed at machine speed using AI and ML, providing the accuracy, speed and scale enterprises need to thwart breaches.

“CrowdStrike leads the way in stopping the most sophisticated attacks with our industry-leading indicators of attack capability, which revolutionized how security teams prevent threats based on adversary behavior, not easily changed indicators,” said Amol Kulkarni, chief product and engineering officer at CrowdStrike. 

“Now, we are changing the game again with the addition of AI-powered indicators of ttack, which enable organizations to harness the power of the CrowdStrike Security Cloud to examine adversary behavior at machine speed and scale to stop breaches in the most effective way possible.” AI-powered IOAs have identified over 20 never-before-seen adversary patterns, which experts have validated and enforced on the Falcon platform for automated detection and prevention. 

What makes CrowdStrike’s approach to using AI as the basis of their IOAs is how effective it’s proving to be at collecting, analyzing and reporting a network’s telemetry data in real time, having a continuously recorded view of all network activity. Source: CrowdStrike.

AI and ML techniques enrich bot-based patch management with contextual intelligence. One of the most innovative areas of cybersecurity today is how the leading cybersecurity providers rely on a combination of AI and ML techniques to locate, inventory and patch endpoints that need updates. Vendors aim to Excellerate bots’ predictive accuracy and ability to identify which endpoints, machines and systems need patching when evaluating the need to take an inventory-based approach to patch management. 

Ivanti’s recent survey on patch management found that 71% of IT and security professionals found patching overly complex and time-consuming, and 53% said that organizing and prioritizing critical vulnerabilities takes up most of their time.

Patch management needs to be more automated if it’s going to be an effective deterrent against ransomware. Taking a data-driven approach to ransomware helps. Nayaki Nayyar, president and chief product officer at Ivanti, is a leading thought leader in this area and has often presented how the most common software errors can lead to ransomware attacks. During RSA, her presentation on how Ivanti Neurons for Risk-Based Patch Management provides contextual intelligence that includes visibility into all endpoints, including those that are cloud- and on-premises based, all from a unified interface, reflects how advanced bot-based match management is coming using AI as a technology foundation.

Ivanti continues to enhance its bot-based approach to patch management with AI- and ML-based improvements, enabling greater contextual intelligence for enterprises managing large-scale device inventories that make manual patching impractical. Source: Ivanti.

Using AI and ML to Excellerate UEM for every device and machine identity. UEM platforms vary in how advanced they are in capitalizing on AI and Ml technologies when protecting them with least-privileged access. The most advanced UEM platforms can integrate with and help enable enterprise-wide microsegmentation, IAM and PAM. AI and ML adoption across enterprises happens fastest with these technologies embedded in platforms and, in the case of Absolute Software, in the firmware of the endpoint devices.

The same holds true for UEM for machine identities. By taking a direct, firmware-based approach to managing machine-based endpoints to enable real-time OS, patch and application updates that are needed to keep each endpoint secure, CISOs gain the visibility and control of endpoints they need. Absolute Software’s Resilience, the industry’s first self-healing zero-trust platform, is noteworthy for its asset management, device and application control, endpoint intelligence, incident reporting and compliance, according to G2 Crowds’ crowdsourced ratings

Ivanti Neurons for UEM relies on AI-enabled bots to seek out machine identities and endpoints and automatically update them unprompted. Ivanti’s approach to self-healing endpoints is also worth noting for how well its UEM platform approach combines AI, ML and bot technologies to deliver unified endpoint and patch management at scale across a global enterprise customer base. 

Additional vendors rated highly by G2 Crowd include CrowdStrike FalconVMware Workspace ONE and others. 

AI and ML are core to zero trust 

Every enterprise’s zero-trust security roadmap will be as unique as its business model and approach. A zero-trust network access (ZTNA) framework needs to be able to flex and change quickly as the business it’s supporting changes direction. Longstanding tech stacks that sought security using interdomain controllers and implicit trust proved too slow to react and be responsive to changing business requirements. 

Relying on implicit trust to connect domains was also an open invitation to a breach. 

What’s needed are cloud-based security platforms that can interpret and act on network telemetry data in real time. CrowdStrike’s Falcon platform, Ivanti’s approach to integrating AI and ML across their product lines, and Microsoft’s approach on Defender365 and their build-out of the functionality on Azure, are examples of what the future of cybersecurity looks like in a zero-trust world. Gaining AI and ML-based insights at machine speed, as CrowdStrike’s new AI-powered IOA does, is what enterprises need to stay secure while pivoting to new business opportunities in the future.

VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Discover our Briefings.

Mon, 28 Nov 2022 15:40:00 -0600 Louis Columbus en-US text/html
Killexams : Microsoft Trends Analysis Helps Marketers Reach Shoppers With New Year's Resolutions

Microsoft Advertising has analyzed future trends to help marketers leverage online activity related to New Year's resolutions, but those developing campaigns can apply the tactics to any industry, from automotive to travel, to prepare December campaigns.

With the start of a new year, consumers are making resolutions to lose weight, …

Tue, 29 Nov 2022 02:00:00 -0600 Laurie Sullivan en text/html
Killexams : Machine Learning in Manufacturing Market Geographical Segmentation By Forecast Revenue 2023-2028

The MarketWatch News Department was not involved in the creation of this content.

Nov 25, 2022 (The Expresswire) -- Final Report will add the analysis of the impact of COVID-19 on this industry.

"Machine Learning in Manufacturing Market" Insights 2022 - By Applications (Automobile, Energy and Power, Pharmaceuticals, Heavy Metals and Machine Manufacturing, Semiconductors and Electronics, Food and Beveragess), By Types (, Hardware, Software, Services, ), By Segmentation analysis, Regions and Forecast to 2028. The Global Machine Learning in Manufacturing market Report provides In-depth analysis on the market status of the Machine Learning in Manufacturing Top manufacturers with best facts and figures, meaning, Definition, SWOT analysis, PESTAL analysis, expert opinions and the latest developments across the globe., the Machine Learning in Manufacturing Market Report contains Full TOC, Tables and Figures, and Chart with Key Analysis, Pre and Post COVID-19 Market Outbreak Impact Analysis and Situation by Regions.

Machine Learning in Manufacturing Market Size is projected to Reach Multimillion USD by 2028, In comparison to 2021, at unexpected CAGR during the forecast Period 2022-2028.

Browse Detailed TOC, Tables and Figures with Charts that provides exclusive data, information, vital statistics, trends, and competitive landscape details in this niche sector.

Considering the economic change due to COVID-19 and Russia-Ukraine War Influence, Machine Learning in Manufacturing, which accounted for % of the global market of Machine Learning in Manufacturing in 2021


Moreover, it helps new businesses perform a positive assessment of their business plans because it covers a range of subjects market participants must be aware of to remain competitive.

Machine Learning in Manufacturing Market Report identifies various key players in the market and sheds light on their strategies and collaborations to combat competition. The comprehensive report provides a two-dimensional picture of the market. By knowing the global revenue of manufacturers, the global price of manufacturers, and the production by manufacturers during the forecast period of 2022 to 2028, the reader can identify the footprints of manufacturers in the Machine Learning in Manufacturing industry.

Machine Learning in Manufacturing Market - Competitive and Segmentation Analysis:

As well as providing an overview of successful marketing strategies, market contributions, and recent developments of leading companies, the report also offers a dashboard overview of leading companies' past and present performance. Several methodologies and analyses are used in the research report to provide in-depth and accurate information about the Machine Learning in Manufacturing Market.

The Major players covered in the Machine Learning in Manufacturing market report are:

● Intel
● Siemens
● GE
● Google
● Microsoft
● Micron Technology
● Amazon Web Services (AWS)
● Nvidia
● Sight Machine

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Short Description About Machine Learning in Manufacturing Market:

The Global Machine Learning in Manufacturing Market is anticipated to rise at a considerable rate during the forecast period, between 2022 and 2028. In 2020, the market is growing at a steady rate and with the rising adoption of strategies by key players, the market is expected to rise over the projected horizon.

This report focuses on global and United States Machine Learning in Manufacturing market, also covers the segmentation data of other regions in regional level and county level.

Due to the COVID-19 pandemic, the global Machine Learning in Manufacturing market size is estimated to be worth USD million in 2022 and is forecast to a readjusted size of USD million by 2028 with a Impressive CAGR during the review period. Fully considering the economic change by this health crisis, by Type, Machine Learning in Manufacturing accounting for % of the Machine Learning in Manufacturing global market in 2021, is projected to value USD million by 2028, growing at a revised % CAGR in the post-COVID-19 period. While by Application, Machine Learning in Manufacturing was the leading segment, accounting for over percent market share in 2021, and altered to an % CAGR throughout this forecast period.


The global Machine Learning in Manufacturing market is projected to reach USD million by 2028 from an estimated USD million in 2022, at a magnificent CAGR during 2023 and 2028.

Report Scope

This report aims to provide a comprehensive presentation of the global market for Machine Learning in Manufacturing, with both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Machine Learning in Manufacturing.

The Machine Learning in Manufacturing market size, estimations, and forecasts are provided in terms of output/shipments (K Units) and revenue (USD millions), considering 2021 as the base year, with history and forecast data for the period from 2017 to 2028. This report segments the global Machine Learning in Manufacturing market comprehensively. Regional market sizes, concerning products by types, by application, and by players, are also provided. The influence of COVID-19 and the Russia-Ukraine War were considered while estimating market sizes.

For a more in-depth understanding of the market, the report provides profiles of the competitive landscape, key competitors, and their respective market ranks. The report also discusses technological trends and new product developments.

The report will help the Machine Learning in Manufacturing manufacturers, new entrants, and industry chain related companies in this market with information on the revenues, production, and average price for the overall market and the sub-segments across the different segments, by company, product type, application, and regions.

Key Companies and Market Share Insights

In this section, the readers will gain an understanding of the key players competing. This report has studied the key growth strategies, such as innovative trends and developments, intensification of product portfolio, mergers and acquisitions, collaborations, new product innovation, and geographical expansion, undertaken by these participants to maintain their presence. Apart from business strategies, the study includes current developments and key financials. The readers will also get access to the data related to global revenue, price, and sales by manufacturers for the period 2017-2022. This all-inclusive report will certainly serve the clients to stay updated and make effective decisions in their businesses.

Get a demo Copy of the Machine Learning in Manufacturing Report 2022

Machine Learning in Manufacturing Market 2022 is segmented as per type of product and application. Each segment is carefully analyzed for exploring its market potential. All of the segments are studied in detail on the basis of market size, CAGR, market share, consumption, revenue and other vital factors.

Global Machine Learning in Manufacturing Market Revenue Led By Product Type Segment:

● ● Hardware
● Software
● Services

Global Machine Learning in Manufacturing Market Leading End-Use Segment:

● Automobile
● Energy and Power
● Pharmaceuticals
● Heavy Metals and Machine Manufacturing
● Semiconductors and Electronics
● Food and Beverages
● Others

Machine Learning in Manufacturing Market is further classified on the basis of region as follows:

● North America (United States, Canada and Mexico) ● Europe (Germany, UK, France, Italy, Russia and Turkey etc.) ● Asia-Pacific (China, Japan, Korea, India, Australia, Indonesia, Thailand, Philippines, Malaysia and Vietnam) ● South America (Brazil, Argentina, Columbia etc.) ● Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

This Machine Learning in Manufacturing Market Research/Analysis Report Contains Answers to your following Questions

● What are the global trends in the Machine Learning in Manufacturing market? Would the market witness an increase or decline in the demand in the coming years? ● What is the estimated demand for different types of products in Machine Learning in Manufacturing? What are the upcoming industry applications and trends for Machine Learning in Manufacturing market? ● What Are Projections of Global Machine Learning in Manufacturing Industry Considering Capacity, Production and Production Value? What Will Be the Estimation of Cost and Profit? What Will Be Market Share, Supply and Consumption? What about Import and Export? ● Where will the strategic developments take the industry in the mid to long-term? ● What are the factors contributing to the final price of Machine Learning in Manufacturing? What are the raw materials used for Machine Learning in Manufacturing manufacturing? ● How big is the opportunity for the Machine Learning in Manufacturing market? How will the increasing adoption of Machine Learning in Manufacturing for mining impact the growth rate of the overall market? ● How much is the global Machine Learning in Manufacturing market worth? What was the value of the market In 2020? ● Who are the major players operating in the Machine Learning in Manufacturing market? Which companies are the front runners? ● Which are the recent industry trends that can be implemented to generate additional revenue streams? ● What Should Be Entry Strategies, Countermeasures to Economic Impact, and Marketing Channels for Machine Learning in Manufacturing Industry?

Customization of the Report

Our research analysts will help you to get customized details for your report, which can be modified in terms of a specific region, application or any statistical details. In addition, we are always willing to comply with the study, which triangulated with your own data to make the market research more comprehensive in your perspective.

Inquire more and share questions if any before the purchase on this report at -

Detailed TOC of Global Machine Learning in Manufacturing Market Insights and Forecast to 2028

1 Study Coverage
1.1 Machine Learning in Manufacturing Product Introduction
1.2 Market by Type
1.2.1 Global Machine Learning in Manufacturing Market Size by Type, 2017 VS 2022 VS 2028
1.3 Market by Application
1.3.1 Global Machine Learning in Manufacturing Market Size by Application, 2017 VS 2022 VS 2028

1.4 Study Objectives
1.5 Years Considered

2 Global Machine Learning in Manufacturing Production
2.1 Global Machine Learning in Manufacturing Production Capacity (2017-2028)
2.2 Global Machine Learning in Manufacturing Production by Region: 2017 VS 2022 VS 2028
2.3 Global Machine Learning in Manufacturing Production by Region
2.3.1 Global Machine Learning in Manufacturing Historic Production by Region (2017-2022)
2.3.2 Global Machine Learning in Manufacturing Forecasted Production by Region (2023-2028)
2.4 North America
2.5 Europe
2.6 China
2.7 Japan

3 Global Machine Learning in Manufacturing Sales in Volume andamp Value Estimates and Forecasts
3.1 Global Machine Learning in Manufacturing Sales Estimates and Forecasts 2017-2028
3.2 Global Machine Learning in Manufacturing Revenue Estimates and Forecasts 2017-2028
3.3 Global Machine Learning in Manufacturing Revenue by Region: 2017 VS 2022 VS 2028
3.4 Global Machine Learning in Manufacturing Sales by Region
3.4.1 Global Machine Learning in Manufacturing Sales by Region (2017-2022)
3.4.2 Global Sales Machine Learning in Manufacturing by Region (2023-2028)
3.5 Global Machine Learning in Manufacturing Revenue by Region
3.5.1 Global Machine Learning in Manufacturing Revenue by Region (2017-2022)
3.5.2 Global Machine Learning in Manufacturing Revenue by Region (2023-2028)
3.6 North America
3.7 Europe
3.8 Asia-Pacific
3.9 Latin America
3.10 Middle East andamp Africa

4 Competition by Manufactures
4.1 Global Machine Learning in Manufacturing Production Capacity by Manufacturers
4.2 Global Machine Learning in Manufacturing Sales by Manufacturers
4.2.1 Global Machine Learning in Manufacturing Sales by Manufacturers (2017-2022)
4.2.2 Global Machine Learning in Manufacturing Sales Market Share by Manufacturers (2017-2022)
4.2.3 Global Top 10 and Top 5 Largest Manufacturers of Machine Learning in Manufacturing in 2022
4.3 Global Machine Learning in Manufacturing Revenue by Manufacturers
4.3.1 Global Machine Learning in Manufacturing Revenue by Manufacturers (2017-2022)
4.3.2 Global Machine Learning in Manufacturing Revenue Market Share by Manufacturers (2017-2022)
4.3.3 Global Top 10 and Top 5 Companies by Machine Learning in Manufacturing Revenue in 2022
4.4 Global Machine Learning in Manufacturing Sales Price by Manufacturers
4.5 Analysis of Competitive Landscape
4.5.1 Manufacturers Market Concentration Ratio (CR5 and HHI)
4.5.2 Global Machine Learning in Manufacturing Market Share by Company Type (Tier 1, Tier 2, and Tier 3)
4.5.3 Global Machine Learning in Manufacturing Manufacturers Geographical Distribution
4.6 Mergers andamp Acquisitions, Expansion Plans

Get a demo Copy of the Machine Learning in Manufacturing Market Report 2022

5 Market Size by Type
5.1 Global Machine Learning in Manufacturing Sales by Type
5.1.1 Global Machine Learning in Manufacturing Historical Sales by Type (2017-2022)
5.1.2 Global Machine Learning in Manufacturing Forecasted Sales by Type (2023-2028)
5.1.3 Global Machine Learning in Manufacturing Sales Market Share by Type (2017-2028)
5.2 Global Machine Learning in Manufacturing Revenue by Type
5.2.1 Global Machine Learning in Manufacturing Historical Revenue by Type (2017-2022)
5.2.2 Global Machine Learning in Manufacturing Forecasted Revenue by Type (2023-2028)
5.2.3 Global Machine Learning in Manufacturing Revenue Market Share by Type (2017-2028)
5.3 Global Machine Learning in Manufacturing Price by Type
5.3.1 Global Machine Learning in Manufacturing Price by Type (2017-2022)
5.3.2 Global Machine Learning in Manufacturing Price Forecast by Type (2023-2028)

6 Market Size by Application
6.1 Global Machine Learning in Manufacturing Sales by Application
6.1.1 Global Machine Learning in Manufacturing Historical Sales by Application (2017-2022)
6.1.2 Global Machine Learning in Manufacturing Forecasted Sales by Application (2023-2028)
6.1.3 Global Machine Learning in Manufacturing Sales Market Share by Application (2017-2028)
6.2 Global Machine Learning in Manufacturing Revenue by Application
6.2.1 Global Machine Learning in Manufacturing Historical Revenue by Application (2017-2022)
6.2.2 Global Machine Learning in Manufacturing Forecasted Revenue by Application (2023-2028)
6.2.3 Global Machine Learning in Manufacturing Revenue Market Share by Application (2017-2028)
6.3 Global Machine Learning in Manufacturing Price by Application
6.3.1 Global Machine Learning in Manufacturing Price by Application (2017-2022)
6.3.2 Global Machine Learning in Manufacturing Price Forecast by Application (2023-2028)

7 Machine Learning in Manufacturing Consumption by Regions
7.1 Global Machine Learning in Manufacturing Consumption by Regions
7.1.1 Global Machine Learning in Manufacturing Consumption by Regions
7.1.2 Global Machine Learning in Manufacturing Consumption Market Share by Regions

8.1 North America
8.1.1 North America Machine Learning in Manufacturing Consumption by Application
8.1.2 North America Machine Learning in Manufacturing Consumption by Countries

9.2 United States
9.2.1 Canada
9.2.2 Mexico

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10.1 Europe
10.1.1 Europe Machine Learning in Manufacturing Consumption by Application
10.1.2 Europe Machine Learning in Manufacturing Consumption by Countries
10.1.3 Germany
10.1.4 France
10.1.5 UK
10.1.6 Italy
10.1.7 Russia

11.1 Asia Pacific
11.1.1 Asia Pacific Machine Learning in Manufacturing Consumption by Application
11.1.2 Asia Pacific Machine Learning in Manufacturing Consumption by Countries
11.1.3 China
11.1.4 Japan
11.1.5 South Korea
11.1.6 India
11.1.7 Australia
11.1.8 Indonesia
11.1.9 Thailand
11.1.10 Malaysia
11.1.11 Philippines
11.1.12 Vietnam

12.1 Central and South America
12.1.1 Central and South America Machine Learning in Manufacturing Consumption by Application
12.1.2 Central and South America Machine Learning in Manufacturing Consumption by Countries
12.1.3 Brazil

13.1 Middle East and Africa
13.1.1 Middle East and Africa Machine Learning in Manufacturing Consumption by Application
13.1.2 Middle East and Africa Machine Learning in Manufacturing Consumption by Countries
13.1.3 Turkey
13.1.4 GCC Countries
13.1.7 Egypt
13.1.6 South Africa

14 Corporate Profiles

14.1.1 Corporation Information
14.1.2 Overview
14.1.3 Machine Learning in Manufacturing Sales, Price, Revenue and Gross Margin (2017-2022)
14.1.4 Machine Learning in Manufacturing Product Model Numbers, Pictures, Descriptions and Specifications
14.1.7 recent Developments

15 Industry Chain and Sales Channels Analysis
15.1 Machine Learning in Manufacturing Industry Chain Analysis
15.2 Machine Learning in Manufacturing Key Raw Materials
15.2.1 Key Raw Materials
15.2.2 Raw Materials Key Suppliers
15.3 Machine Learning in Manufacturing Production Mode andamp Process
15.4 Machine Learning in Manufacturing Sales and Marketing
15.4.1 Machine Learning in Manufacturing Sales Channels
15.4.2 Machine Learning in Manufacturing Distributors
15.7 Machine Learning in Manufacturing Customers

16 Market Drivers, Opportunities, Challenges and Risks Factors Analysis
16.1 Machine Learning in Manufacturing Industry Trends
16.2 Machine Learning in Manufacturing Market Drivers
16.3 Machine Learning in Manufacturing Market Challenges
16.4 Machine Learning in Manufacturing Market Restraints

17 Key Finding in The Global Machine Learning in Manufacturing Study

18 Appendix
18.1 Research Methodology
18.1.1 Methodology/Research Approach
18.1.2 Data Source
18.2 Author Details
18.3 Disclaimer

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Fri, 25 Nov 2022 10:19:00 -0600 en-US text/html
Killexams : Corporate e-learning market size to grow by No result found, try new keyword!Technavio categorizes the global corporate e-learning market as a part of the education services market, the parent market. The education services market covers products, services, and solutions that ... Wed, 30 Nov 2022 20:22:00 -0600 en-US text/html Killexams : Analyzing Board-Level Cybersecurity Experience
Executive Summary

In March 2022, the U.S. Securities and Exchange Commission proposed rules that would require public companies to make standardized disclosures on cybersecurity risk management, strategy, governance, and incident reporting. Under the proposed rules companies would need to disclose at least annually whether their board directors have expertise in cybersecurity.

‘Expertise’ is not defined by the SEC, however according to the proposed rule: “if any member of the board...

Tue, 29 Nov 2022 02:39:00 -0600 Rob Sloan en-US text/html
Killexams : The cloud in orbit: Amazon Web Services demonstrates data analysis on a satellite An artist’s conception shows D-Orbit’s ION spacecraft deploying smaller satellites. (D-Orbit Illustration) © Provided by Geekwire An artist’s conception shows D-Orbit’s ION spacecraft deploying smaller satellites. (D-Orbit Illustration)

For the past 10 months, Amazon Web Services has been running data through its cloud-based software platform on what’s arguably the world’s edgiest edge: a satellite in low Earth orbit.

The experiment, revealed today during AWS’ re:Invent conference in Las Vegas, is aimed at demonstrating how on-orbit processing can help satellite operators manage the torrents of imagery and sensor data generated by their spacecraft.

“Using AWS software to perform real-time data analysis onboard an orbiting satellite, and delivering that analysis directly to decision makers via the cloud, is a definite shift in existing approaches to space data management,” Max Peterson, AWS’ vice president of worldwide public sector, said today in a blog posting. “It also helps push the boundaries of what we believe is possible for satellite operations.”

AWS’ experiment was done in partnership with D-Orbit, an Italian-based company that focuses on space logistics and transportation; and with Unibap, a Swedish company that develops AI-enabled automation solutions for space-based as well as terrestrial applications.

Software tools from AWS — including the company’s machine learning models and AWS IoT Greengrass — were integrated into a prototype processing payload built by Unibap. That payload was then placed on D-Orbit’s ION satellite carrier. The ION spacecraft was one of scores of spacecraft sent into orbit aboard a SpaceX Falcon 9 rocket in January. A few weeks after deploying its satellites, D-Orbit’s ION carrier ramped up data processing operations on the payload using AWS’ software.

During the D-Orbit ION experiment, the team applied various machine learning models to satellite sensor data to identify specific types of objects in the sky, such as clouds and wildfires, as well as terrestrial objects including buildings and ships.

AWS said that its artificial intelligence and machine learning services helped reduce the size of images by up to 42 percent, resulting in increased processing speeds. The AI system could decide in real time which satellite images should be given high priority for downlinking, and which images could be set aside.

The team also modified the process for sending data to and from the satellite, to build in more tolerance for communication delays. The modification made it simpler to manage file transfers automatically, without having to manually process downlinks over multiple ground station contacts.

This isn’t AWS’ only foray into space-based cloud computing: Amazon sent an edge-computing device known as an AWS Snowcone to the International Space Station in April during Axiom Space’s first private space mission. (Axiom Space is also partnering with Microsoft Azure and LEOCloud on a separate project to put cloud infrastructure in orbit.)

D-Orbit’s vice president of commercial sales, Sergio Mucciarelli, said there’s a lot of value in being able to process data in space.

“Our customers want to securely process increasingly large amounts of satellite data with very low latency,” Mucciarelli explained. “This is something that is limited by using legacy methods, downlinking all data for processing on the ground. We believe in the drive toward edge computing, and we believe it can only be done with space-based infrastructure that is fit for purpose, giving customers a high degree of confidence that they can run their workloads and operations reliably in the harsh space operating environment.”

Fredrik Bruhn, Unibap’s chief evangelist for digital transformation, said his company wants to help customers turn raw satellite data into “actionable information that can be used to disseminate alerts in seconds.”

“Providing users real-time access to AWS edge services and capabilities on orbit will allow them to gain more timely insights and optimize how they use their satellite and ground resources,” Bruhn said.

AWS, Unibap and D-Orbit are continuing to test new capabilities on the ION platform — including new approaches for processing raw data on orbit, as well as more refined methods for data delivery. If the experiment bears fruit, it could soon become routine for satellites to make sense of what they’re seeing before they downlink the data.

Tue, 29 Nov 2022 02:30:00 -0600 en-US text/html
Killexams : The Worldwide Machine Learning as a Service Industry is Expected to Reach $36.2 Billion by 2028

DUBLIN, Dec. 6, 2022 /PRNewswire/ -- The "Global Machine Learning as a Service Market Size, Share & Industry Trends Analysis Report By End User, By Offering, By Organization Size, By Application, By Regional Outlook and Forecast, 2022 - 2028" report has been added to's offering.

The Global Machine learning as a Service Market size is expected to reach $36.2 billion by 2028, rising at a market growth of 31.6% CAGR during the forecast period.

Machine learning is a data analysis method that includes statistical data analysis to create desired prediction output without the use of explicit programming. It uses a sequence of algorithms to comprehend the link between datasets in order to produce the desired result. It is designed to include artificial intelligence (AI) and cognitive computing functionalities. Machine learning as a service (MLaaS) refers to a group of cloud computing services that provide machine learning technologies.

Increased demand for cloud computing, as well as growth connected with artificial intelligence and cognitive computing, are major machine learning as service industry growth drivers. Growth in demand for cloud-based solutions, such as cloud computing, rise in adoption of analytical solutions, growth of the artificial intelligence & cognitive computing market, increased application areas, and a scarcity of trained professionals are all influencing the machine learning as a service market.

As more businesses migrate their data from on-premise storage to cloud storage, the necessity for efficient data organization grows. Since MLaaS platforms are essentially cloud providers, they enable solutions to appropriately manage data for machine learning experiments and data pipelines, making it easier for data engineers to access and process the data.

For organizations, MLaaS providers offer capabilities like data visualization and predictive analytics. They also provide APIs for sentiment analysis, facial recognition, creditworthiness evaluations, corporate intelligence, and healthcare, among other things. The actual computations of these processes are abstracted by MLaaS providers, so data scientists don't have to worry about them. For machine learning experimentation and model construction, some MLaaS providers even feature a drag-and-drop interface.

COVID-19 Impact Analysis

The COVID-19 pandemic has had a substantial impact on numerous countries' health, economic, and social systems. It has resulted in millions of fatalities across the globe and has left the economic and financial systems in tatters. Individuals can benefit from knowledge about individual-level susceptibility variables in order to better understand and cope with their psychological, emotional, and social well-being.

Artificial intelligence technology is likely to aid in the fight against the COVID-19 pandemic. COVID-19 cases are being tracked and traced in several countries utilizing population monitoring approaches enabled by machine learning and artificial intelligence. Researchers in South Korea, for example, track coronavirus cases using surveillance camera footage and geo-location data.

Market Growth Factors

Increased Demand for Cloud Computing and a Boom in Big Data

The industry is growing due to the increased acceptance of cloud computing technologies and the use of social media platforms. Cloud computing is now widely used by all companies that supply enterprise storage solutions. Data analysis is performed online using cloud storage, giving the advantage of evaluating real-time data collected on the cloud.

Cloud computing enables data analysis from any location and at any time. Moreover, using the cloud to deploy machine learning allows businesses to get useful data, such as consumer behavior and purchasing trends, virtually from linked data warehouses, lowering infrastructure and storage costs. As a result, the machine learning as a service business is growing as cloud computing technology becomes more widely adopted.

Use of Machine Learning to Fuel Artificial Intelligence Systems

Machine learning is used to fuel reasoning, learning, and self-correction in artificial intelligence (AI) systems. Expert systems, speech recognition, and machine vision are examples of AI applications. The rise in the popularity of AI is due to current efforts such as big data infrastructure and cloud computing.

Top companies across industries, including Google, Microsoft, and Amazon (Software & IT); Bloomberg, American Express (Financial Services); and Tesla and Ford (Automotive), have identified AI and cognitive computing as a key strategic driver and have begun investing in machine learning to develop more advanced systems. These top firms have also provided financial support to young start-ups in order to produce new creative technology.

Market Restraining Factors

Technical Restraints and Inaccuracies of ML

The ML platform provides a plethora of advantages that aid in market expansion. However, several parameters on the platform are projected to impede market expansion. The presence of inaccuracy in these algorithms, which are sometimes immature and underdeveloped, is one of the market's primary constraining factors.

In the big data and machine learning manufacturing industries, precision is crucial. A minor flaw in the algorithm could result in incorrect items being produced. This is expected to exorbitantly increase the operational costs for the owner of the manufacturing unit than decrease it.

Key subjects Covered:

 Chapter 1. Market Scope & Methodology

Chapter 2. Market Overview
2.1 Introduction
2.1.1 Overview Market Composition and Scenario
2.2 Key Factors Impacting the Market
2.2.1 Market Drivers
2.2.2 Market Restraints

Chapter 3. Competition Analysis - Global
3.1 KBV Cardinal Matrix
3.2 recent Industry Wide Strategic Developments
3.2.1 Partnerships, Collaborations and Agreements
3.2.2 Product Launches and Product Expansions
3.2.3 Acquisition and Mergers
3.3 Market Share Analysis, 2021
3.4 Top Winning Strategies
3.4.1 Key Leading Strategies: Percentage Distribution (2018-2022)
3.4.2 Key Strategic Move: (Product Launches and Product Expansions : 2018, Jan - 2022, May) Leading Players
3.4.3 Key Strategic Move: (Partnership, Collaboration and Agreement : 2019, Apr - 2022, Mar) Leading Players

Chapter 4. Global Machine learning as a Service Market by End User
4.1 Global IT & Telecom Market by Region
4.2 Global BFSI Market by Region
4.3 Global Manufacturing Market by Region
4.4 Global Retail Market by Region
4.5 Global Healthcare Market by Region
4.6 Global Energy & Utilities Market by Region
4.7 Global Public Sector Market by Region
4.8 Global Aerospace & Defense Market by Region
4.9 Global Other End User Market by Region

Chapter 5. Global Machine learning as a Service Market by Offering
5.1 Global Services Only Market by Region
5.2 Global Solution (Software Tools) Market by Region

Chapter 6. Global Machine learning as a Service Market by Organization Size
6.1 Global Large Enterprises Market by Region
6.2 Global Small & Medium Enterprises Market by Region

Chapter 7. Global Machine learning as a Service Market by Application
7.1 Global Marketing & Advertising Market by Region
7.2 Global Fraud Detection & Risk Management Market by Region
7.3 Global Computer vision Market by Region
7.4 Global Security & Surveillance Market by Region
7.5 Global Predictive analytics Market by Region
7.6 Global Natural Language Processing Market by Region
7.7 Global Augmented & Virtual Reality Market by Region
7.8 Global Others Market by Region

Chapter 8. Global Machine learning as a Service Market by Region

Chapter 9. Company Profiles
9.1 Hewlett Packard Enterprise Company
9.1.1 Company Overview
9.1.2 Financial Analysis
9.1.3 Segmental and Regional Analysis
9.1.4 Research & Development Expense
9.1.5 recent strategies and developments: Product Launches and Product Expansions: Acquisition and Mergers:
9.2 Oracle Corporation
9.2.1 Company Overview
9.2.2 Financial Analysis
9.2.3 Segmental and Regional Analysis
9.2.4 Research & Development Expense
9.2.5 SWOT Analysis
9.3 Google LLC
9.3.1 Company Overview
9.3.2 Financial Analysis
9.3.3 Segmental and Regional Analysis
9.3.4 Research & Development Expense
9.3.5 recent strategies and developments: Partnerships, Collaborations, and Agreements: Product Launches and Product Expansions:
9.4 Amazon Web Services, Inc. (, Inc.)
9.4.1 Company Overview
9.4.2 Financial Analysis
9.4.3 Segmental Analysis
9.4.4 recent strategies and developments: Partnerships, Collaborations, and Agreements: Product Launches and Product Expansions:
9.5 IBM Corporation
9.5.1 Company Overview
9.5.2 Financial Analysis
9.5.3 Regional & Segmental Analysis
9.5.4 Research & Development Expenses
9.5.5 recent strategies and developments: Partnerships, Collaborations, and Agreements:
9.6 Microsoft Corporation
9.6.1 Company Overview
9.6.2 Financial Analysis
9.6.3 Segmental and Regional Analysis
9.6.4 Research & Development Expenses
9.6.5 recent strategies and developments: Partnerships, Collaborations, and Agreements: Product Launches and Product Expansions:
9.7 Fair Isaac Corporation (FICO)
9.7.1 Company Overview
9.7.2 Financial Analysis
9.7.3 Segmental and Regional Analysis
9.7.4 Research & Development Expenses
9.8 SAS Institute, Inc.
9.8.1 Company Overview
9.8.2 recent strategies and developments: Partnerships, Collaborations, and Agreements:
9.9 Yottamine Analytics, LLC
9.9.1 Company Overview
9.10. BigML
9.10.1 Company Overview

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