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Exam Code: CLF-C01 Practice exam 2022 by Killexams.com team
CLF-C01 AWS Certified Cloud Practitioner (CLF-C01)

Format : Multiple choice, multiple answer
Type : Foundational
Delivery Method : Testing center or online proctored exam
Time : 90 minutes to complete the exam

Introduction
The AWS Certified Cloud Practitioner (CLF-C01) examination is intended for individuals who have the knowledge, skills, and abilities to demonstrate basic knowledge of the AWS platform, including: available services and their common use cases, AWS Cloud architectural principles (at the conceptual level), account security, and compliance. The candidate will demonstrate an understanding of AWS Cloud economics including: costs, billing, and analysis, and the value proposition of the AWS Cloud.

The AWS Certified Cloud Practitioner examination is intended for individuals who have the knowledge and skills necessary to effectively demonstrate an overall understanding of the AWS Cloud, independent of specific technical roles addressed by other AWS Certifications. The exam can be taken at a testing center or from the comfort and convenience of a home or office location as an online proctored exam.

Abilities Validated by the Certification
- Define what the AWS Cloud is and the basic global infrastructure
- Describe basic AWS Cloud architectural principles
- Describe the AWS Cloud value proposition
- Describe key services on the AWS platform and their common use cases (for example, compute and analytics)
- Describe basic security and compliance aspects of the AWS platform and the shared security model
- Define the billing, account management, and pricing models
- Identify sources of documentation or technical assistance (for example, whitepapers or support tickets)
- Describe basic/core characteristics of deploying and operating in the AWS Cloud

Response Types
There are two types of questions on the examination:
 Multiple choice: Has one correct response and three incorrect responses (distractors).
 Multiple response: Has two or more correct responses out of five or more options.
Select one or more responses that best complete the statement or answer the question. Distractors, or incorrect answers, are response options that an examinee with incomplete knowledge or skill would likely choose. However, they are generally plausible responses that fit in the content area defined by the test objective.
Unanswered questions are scored as incorrect; there is no penalty for guessing.

Unscored Content
Your examination may include unscored items that are placed on the test to gather statistical information. These items are not identified on the form and do not affect your score.

Exam Results
The AWS Certified Cloud Practitioner (CLF-C01) examination is a pass or fail exam. The examination is scored against a minimum standard established by AWS professionals who are guided by certification industry best practices and guidelines.
Your results for the examination are reported as a score from 100–1,000, with a minimum passing score of 700. Your score shows how you performed on the examination as a whole and whether or not you passed. Scaled scoring models are used to equate scores across multiple exam forms that may have slightly different difficulty levels.
Your score report contains a table of classifications of your performance at each section level. This information is designed to provide general feedback concerning your examination performance. The examination uses a compensatory scoring model, which means that you do not need to “pass” the individual sections, only the overall examination. Each section of the examination has a specific weighting, so some sections have more questions than others. The table contains general information, highlighting your strengths and weaknesses. Exercise caution when interpreting section-level feedback.

Domain 1: Cloud Concepts 26%
Domain 2: Security and Compliance 25%
Domain 3: Technology 33%
Domain 4: Billing and Pricing 16%
TOTAL 100%

Domain 1: Cloud Concepts
1.1 Define the AWS Cloud and its value proposition
1.2 Identify aspects of AWS Cloud economics
1.3 List the different cloud architecture design principles
Domain 2: Security and Compliance
2.1 Define the AWS shared responsibility model
2.2 Define AWS Cloud security and compliance concepts
2.3 Identify AWS access management capabilities
2.4 Identify resources for security support
Domain 3: Technology
3.1 Define methods of deploying and operating in the AWS Cloud
3.2 Define the AWS global infrastructure
3.3 Identify the core AWS services
3.4 Identify resources for technology support
Domain 4: Billing and Pricing
4.1 Compare and contrast the various pricing models for AWS
4.2 Recognize the various account structures in relation to AWS billing and pricing
4.3 Identify resources available for billing support

AWS Certified Cloud Practitioner (CLF-C01)
Amazon Practitioner learn
Killexams : Amazon Practitioner learn - BingNews https://killexams.com/pass4sure/exam-detail/CLF-C01 Search results Killexams : Amazon Practitioner learn - BingNews https://killexams.com/pass4sure/exam-detail/CLF-C01 https://killexams.com/exam_list/Amazon Killexams : AWS updates its machine learning service SageMaker

Amazon Web Services on Wednesday added new features to its managed machine learning service Amazon SageMaker, designed to Boost governance attributes within the service and adding new capabilities to its notebooks.

Notebooks in context of Amazon SageMaker are compute instances that run the Jupyter Notebook application.

Governance updates to Boost granular access, Boost workflow

AWS said the new features will allow enterprises to scale governance across their ML model lifecycle. As the number of machine learning models increases, it can get challenging for enterprises to manage the task of setting privilege access controls and establishing governance processes to document model information, such as input data sets, training environment information, model-use description, and risk rating.

Data engineering and machine learning teams currently use spreadsheets or ad hoc lists to navigate access policies needed for all processes involved. This can become complex as the size of machine learning teams increases within an enterprise, AWS said in a statement.

Another challenge is to monitor the deployed models for bias and ensure they are performing as expected, the company said.

To tackle these challenges, the cloud services provider has added Amazon SageMaker Role Manager to make it easier for administrators to control access and define permission for users.

Copyright © 2022 IDG Communications, Inc.

Wed, 30 Nov 2022 21:11:00 -0600 en text/html https://www.infoworld.com/article/3681891/aws-updates-its-machine-learning-service-sagemaker.html
Killexams : Amazon SageMaker gets eight new capabilities

Eight new capabilities have been unveiled for Amazon SageMaker, AWS’s end-to-end machine learning (ML) service. Developers, data scientists, and business analysts use Amazon SageMaker to build, train, and deploy ML models quickly and easily using its fully managed infrastructure, tools, and workflows.

The new features include new Amazon SageMaker governance capabilities that provide visibility into model performance throughout the ML lifecycle. New Amazon SageMaker Studio Notebook capabilities provide an enhanced notebook experience that enables customers to inspect and address data-quality issues in just a few clicks, facilitate real-time collaboration across data science teams, and accelerate the process of going from experimentation to production by converting notebook code into automated jobs. Finally, new capabilities within Amazon SageMaker automate model validation and make it easier to work with geospatial data.

“Today, tens of thousands of customers of all sizes and across industries rely on Amazon SageMaker. AWS customers are building millions of models, training models with billions of parameters, and generating trillions of predictions every month. Many customers are using ML at a scale that was unheard of just a few years ago,” said Bratin Saha, vice president of Artificial Intelligence and Machine Learning at AWS. “The new Amazon SageMaker capabilities announced today make it even easier for teams to expedite the end-to-end development and deployment of ML models. From purpose-built governance tools to a next-generation notebook experience and streamlined model testing to enhanced support for geospatial data, we are building on Amazon SageMaker’s success to help customers take advantage of ML at scale.”

New ML governance capabilities in Amazon SageMaker

Amazon SageMaker offers new capabilities that help customers more easily scale governance across the ML model lifecycle. As the number of models and users within an organization increases, it becomes harder to set least-privilege access controls and establish governance processes to document model information (e.g., input data sets, training environment information, model-use description, and risk rating). Once models are deployed, customers also need to monitor for bias and feature drift to ensure they perform as expected.

Amazon SageMaker Role Manager makes it easier to control access and permissions: Appropriate user-access controls are a cornerstone of governance and support data privacy, prevent information leaks, and ensure practitioners can access the tools they need to do their jobs. Implementing these controls becomes increasingly complex as data science teams swell to dozens or even hundreds of people. ML administrators—individuals who create and monitor an organization’s ML systems—must balance the push to streamline development while controlling access to tasks, resources, and data within ML workflows.

Today, administrators create spreadsheets or use ad hoc lists to navigate access policies needed for dozens of different activities (e.g., data prep and training) and roles (e.g., ML engineer and data scientist). Maintaining these tools is manual, and it can take weeks to determine the specific tasks new users will need to do their jobs effectively. Amazon SageMaker Role Manager makes it easier for administrators to control access and define permissions for users. Administrators can select and edit prebuilt templates based on various user roles and responsibilities. The tool then automatically creates the access policies with necessary permissions within minutes, reducing the time and effort to onboard and manage users over time.

Amazon SageMaker Model Cards simplify model information gathering: Today, most practitioners rely on disparate tools (e.g., email, spreadsheets, and text files) to document the business requirements, key decisions, and observations during model development and evaluation. Practitioners need this information to support approval workflows, registration, audits, customer inquiries, and monitoring, but it can take months to gather these details for each model. Some practitioners try to solve this by building complex recordkeeping systems, which is manual, time consuming, and error-prone.

Amazon SageMaker Model Cards provide a single location to store model information in the AWS console, streamlining documentation throughout a model’s lifecycle. The new capability auto-populates training details like input datasets, training environment, and training results directly into Amazon SageMaker Model Cards. Practitioners can also include additional information using a self-guided questionnaire to document model information (e.g., performance goals, risk rating), training and evaluation results (e.g., bias or accuracy measurements), and observations for future reference to further Boost governance and support the responsible use of ML.

Amazon SageMaker Model Dashboard provides a central interface to track ML models: Once a model has been deployed to production, practitioners want to track their model over time to understand how it performs and to identify potential issues. This task is normally done on an individual basis for each model, but as an organization starts to deploy thousands of models, this becomes increasingly complex and requires more time and resources.

Amazon SageMaker Model Dashboard provides a comprehensive overview of deployed models and endpoints, enabling practitioners to track resources and model behavior in one place. From the dashboard, customers can also use built-in integrations with Amazon SageMaker Model Monitor (AWS’s model and data drift monitoring capability) and Amazon SageMaker Clarify (AWS’s ML bias-detection capability). This end-to-end visibility into model behavior and performance provides the necessary information to streamline ML governance processes and quickly troubleshoot model issues.

Next-generation Notebooks

Amazon SageMaker Studio Notebook gives practitioners a fully managed notebook experience, from data exploration to deployment. As teams grow in size and complexity, dozens of practitioners may need to collaboratively develop models using notebooks. AWS continues to offer the best notebook experience for users with the launch of three new features that help customers coordinate and automate their notebook code.

Simplified data preparation: Practitioners want to explore datasets directly in notebooks to spot and correct potential data-quality issues (e.g., missing information, extreme values, skewed datasets, and biases) as they prepare data for training. Practitioners can spend months writing boilerplate code to visualize and examine different parts of their dataset to identify and fix problems. Amazon SageMaker Studio Notebook now offers a built-in data preparation capability that allows practitioners to visually review data characteristics and remediate data-quality problems in just a few clicks—all directly in their notebook environment. When users display a data frame (i.e., a tabular representation of data) in their notebook, Amazon SageMaker Studio Notebook automatically generates charts to help users identify data-quality issues and suggests data transformations to help fix common problems. Once the practitioner selects a data transformation, Amazon SageMaker Studio Notebook generates the corresponding code within the notebook so it can be repeatedly applied every time the notebook is run.

Accelerate collaboration across data science teams: After data has been prepared, practitioners are ready to start developing a model—an iterative process that may require teammates to collaborate within a single notebook. Today, teams must exchange notebooks and other assets (e.g., models and datasets) over email or chat applications to work on a notebook together in real time, leading to communication fatigue, delayed feedback loops, and version-control issues.

Amazon SageMaker now gives teams a workspace where they can read, edit, and run notebooks together in real time to streamline collaboration and communication. Teammates can review notebook results together to immediately understand how a model performs, without passing information back and forth. With built-in support for services like BitBucket and AWS CodeCommit, teams can easily manage different notebook versions and compare changes over time. Affiliated resources, like experiments and ML models, are also automatically saved to help teams stay organized.

Automatic conversion of notebook code to production-ready jobs: When practitioners want to move a finished ML model into production, they usually copy snippets of code from the notebook into a script, package the script with all its dependencies into a container, and schedule the container to run. To run this job repeatedly on a schedule, they must set up, configure, and manage a continuous integration and continuous delivery (CI/CD) pipeline to automate their deployments. It can take weeks to get all the necessary infrastructure set up, which takes time away from core ML development activities.

Amazon SageMaker Studio Notebook now allows practitioners to select a notebook and automate it as a job that can run in a production environment. Once a notebook is selected, Amazon SageMaker Studio Notebook takes a snapshot of the entire notebook, packages its dependencies in a container, builds the infrastructure, runs the notebook as an automated job on a schedule set by the practitioner, and deprovisions the infrastructure upon job completion, reducing the time it takes to move a notebook to production from weeks to hours.

Automated validation of new models using real-time inference requests: Before deploying to production, practitioners test and validate every model to check performance and identify errors that could negatively impact the business. Typically, they use historical inference request data to test the performance of a new model, but this data sometimes fails to account for current, real-world inference requests. For example, historical data for an ML model to plan the fastest route might fail to account for an accident or a sudden road closure that significantly alters the flow of traffic.

Read also: Here are five new database and analytics capabilities on AWS

To address this issue, practitioners route a copy of the inference requests going to a production model to the new model they want to test. It can take weeks to build this testing infrastructure, mirror inference requests, and compare how models perform across key metrics (e.g., latency and throughput). While this provides practitioners with greater confidence in how the model will perform, the cost and complexity of implementing these solutions for hundreds or thousands of models makes it unscalable.

Amazon SageMaker Inference now provides a capability to make it easier for practitioners to compare the performance of new models against production models, using the same real-world inference request data in real time. Now, they can easily scale their testing to thousands of new models simultaneously, without building their own testing infrastructure. To start, a customer selects the production model they want to test against, and Amazon SageMaker Inference deploys the new model to a hosting environment with the exact same conditions.

Amazon SageMaker routes a copy of the inference requests received by the production model to the new model and creates a dashboard to display performance differences across key metrics, so customers can see how each model differs in real time. Once the customer validates the new model’s performance and is confident it is free of potential errors, they can safely deploy it.

New geospatial capabilities in Amazon SageMaker make it easier for customers to make predictions using satellite and location data: Today, most data captured has geospatial information (e.g., location coordinates, weather maps, and traffic data). However, only a small amount of it is used for ML purposes because geospatial datasets are difficult to work with and can often be petabytes in size, spanning entire cities or hundreds of acres of land.

To start building a geospatial model, customers typically augment their proprietary data by procuring third-party data sources like satellite imagery or map data. Practitioners need to combine this data, prepare it for training, and then write code to divide datasets into manageable subsets due to the massive size of geospatial data. Once customers are ready to deploy their trained models, they must write more code to recombine multiple datasets to correlate the data and ML model predictions.

To extract predictions from a finished model, practitioners then need to spend days using open source visualization tools to render on a map. The entire process from data enrichment to visualization can take months, which makes it hard for customers to take advantage of geospatial data and generate timely ML predictions.

Amazon SageMaker now accelerates and simplifies generating geospatial ML predictions by enabling customers to enrich their datasets, train geospatial models, and visualize the results in hours instead of months. With just a few clicks or using an API, customers can use Amazon SageMaker to access a range of geospatial data sources from AWS (e.g., Amazon Location Service), open-source datasets (e.g., Amazon Open Data), or their own proprietary data including from third-party providers (like Planet Labs). Once a practitioner has selected the datasets they want to use, they can take advantage of built-in operators to combine these datasets with their own proprietary data. To speed up model development, Amazon SageMaker provides access to pre-trained deep-learning models for use cases such as increasing crop yields with precision agriculture, monitoring areas after natural disasters, and improving urban planning. After training, the built-in visualization tool displays data on a map to uncover new predictions.

Thu, 01 Dec 2022 07:04:00 -0600 Caleb Ojewale en-US text/html https://businessday.ng/technology/article/amazon-sagemaker-gets-eight-new-capabilities/
Killexams : AWS adds machine learning capabilities to Amazon Connect

In a bid to help enterprises offer better customer service and experience, Amazon Web Services (AWS) on Tuesday, at its annual re:Invent conference, said that it was adding new machine learning capabilities to its cloud-based contact center service, Amazon Connect.

AWS launched Amazon Connect in 2017 in an effort to offer a low-cost, high-value alternative to traditional customer service software suites.

As part of the announcement, the company said that it was making the forecasting, capacity planning, scheduling and Contact Lens feature of Amazon Connect generally available while introducing two new features in preview.

Forecasting, capacity planning and scheduling now available

The forecasting, capacity planning and scheduling features, which were announced in March and have been in preview until now, are geared toward helping enterprises predict contact center demand, plan staffing, and schedule agents as required.

In order to forecast demand, Amazon Connect uses machine learning models to analyze and predict contact volume and average handle time based on historical data, the company said, adding that the forecasts include predictions for inbound calls, transfer calls, and callback contacts in both voice and chat channels.

These forecasts are then combined with planning scenarios and metrics such as occupancy, daily attrition, and full-time equivalent (FTE) hours per week to help with staffing, the company said, adding that the capacity planning feature helps predict the number of agents required to meet service level targets for a certain period of time.

Amazon Connect uses the forecasts generated from historical data and combines them with metrics or inputs such as shift profiles and staffing groups to create schedules that match an enterprise’s requirements.

The schedules created can be edited or reviewed if needed and once the schedules are published, Amazon Connect notifies the agent and the supervisor that a new schedule has been made available.

Additionally, the scheduling feature now supports intraday agent request management which helps track time off or overtime for agents.

A machine learning model at the back end that drives scheduling can make real-time adjustments in context of the rules input by an enterprise, AWS said, adding that enterprises can take advantage of the new features by enabling them at the Amazon Connect Console.

After they have been activated via the Console, the capabilities can be accessed via the Amazon Connect Analytics and Optimization module within Connect.

The forecasting, capacity planning, and scheduling features are available initially across US East (North Virginia), US West (Oregon), Asia Pacific (Sydney), and Europe (London) Regions.

The Contact Lens service, which was added to Amazon Connect to analyze conversations in real time using natural language processing (NLP) and speech-to-text analytics, has been made generally available.

The capability to do analysis has been extended to text messages from Amazon Connect Chat, AWS said.

Contact Lens’ conversational analytics for chat helps you understand customer sentiment, redact sensitive customer information, and monitor agent compliance with company guidelines to Boost agent performance and customer experience,” the company said in a statement.

Another feature within Contact Lens, dubbed contact search, will allow enterprises to search for chats based on specific keywords, customer sentiment score, contact categories, and other chat-specific analytics such as agent response time, the company said, adding that Lens will also offer a chat summarization feature.

This feature, according to the company, uses machine learning to classify, and highlight key parts of the customer’s conversation, such as issue, outcome, or action item.

New features allow for agent evaluation

AWS also said that it was adding two new capabilities—evaluating agents and recreating contact center workflow—to Amazon Connect, in preview. Using Contact Lens for Amazon Connect, enterprises will be able to create agent performance evaluation forms, the company said, adding that the service is now in preview and available across regions including  US East (North Virginia), US West (Oregon), Asia Pacific (Sydney), and Europe (London).

New evaluation criteria, such as agents’ adherence to scripts and compliance, can be added to the review forms, AWS said, adding that machine-learning based scoring can be activated.

The machine learning scoring will use the same underlying technology used by Contact Lens to analyze conversations.

Additionally, AWS said that it was giving enterprises the chance to create new workflows for agents who use the Amazon Connect Agent Workspace to do daily tasks.

“You can now also use Amazon Connect’s no-code, drag-and-drop interface to create custom workflows and step-by-step guides for your agents,” the company said in a statement.

Amazon Connect uses a pay-for-what-you-use model, and no upfront payments or long-term commitments are required to sign up for the service.

Tue, 29 Nov 2022 18:25:00 -0600 Author: Anirban Ghoshal en-US text/html https://www.cio.com/article/414715/aws-adds-machine-learning-capabilities-to-amazon-connect.html
Killexams : Weights and Biases Joins the Amazon SageMaker Ready Program

SAN FRANCISCO, Dec. 1, 2022 /PRNewswire/ -- Weights & Biases, the developer-first MLOps platform, announced today it has joined the Amazon SageMaker Ready Program. This designation helps customers discover partner software solutions that are validated by Amazon Web Services (AWS) Partner Solutions Architects to integrate with Amazon SageMaker.

Joining the Amazon SageMaker Ready Program differentiates Weights & Biases as an AWS Partner Network (APN) member with a product that works with Amazon SageMaker and is generally available for and fully supports AWS customers. The Amazon SageMaker Ready Program helps customers quickly and easily find AWS Partners that can help accelerate their machine learning (ML) adoption by providing out-of-the-box abstractions for most common challenges in ML that build on top of the foundational capabilities Amazon SageMaker provides.

Amazon SageMaker offers a robust set of capabilities and AWS Partners help extend its value by integrating these capabilities with their solutions. By providing customers with a catalog of solutions that lift the complexities of machine learning, the Amazon SageMaker Ready Program will broaden the user base and increase customer adoption. Amazon SageMaker Ready Program members offer AWS customers Amazon SageMaker-supported products that offer Amazon SageMaker both in AWS Partner solutions they already know, or offer products that simplify each step of the ML model building. These applications are validated by AWS Partner Solutions Architects to ensure customers have a consistent experience using the software.

Customers can review the Amazon SageMaker Ready Partner product catalog to confirm their preferred vendor solutions are already integrated with Amazon SageMaker. Customers can also discover, browse by category or ML model deployment challenges, and select partner software solutions for their specific ML development needs.

"Weights & Biases is proud to be an early Amazon SageMaker Service Ready Partner," said Seann Gardiner, VP, Business Development, Weights & Biases. "Our team is dedicated to helping Amazon SageMaker customers Boost their MLOps processes with the Weights and Biases platform to accelerate machine learning, deep learning, and AI development. This designation underscores our commitment to helping customers achieve their ML objectives by leveraging the agility, breadth of services, and pace of innovation that AWS provides." ."

To support the seamless integration and deployment of these solutions, AWS established the AWS Service Ready Program to help customers identify solutions that support AWS services and spend less time evaluating new tools, and more time scaling their use of solutions that work on AWS.

Weights & Biases helps ML teams leveraging Amazon SageMaker unlock their productivity by optimizing, visualizing, collaborating on, and standardizing their model and data pipelines – regardless of framework, environment, or workflow. With a few lines of code, ML practitioners save everything they need to debug, compare and reproduce models — architecture, hyperparameters, git commits, model weights, GPU usage, and even datasets and predictions. For more information, please visit https://wandb.ai/site/aws

About Weights & Biases

Weights & Biases is the leading developer-first MLOps platform that provides enterprise-grade, end-to-end MLOps workflow to accelerate ML activities. Used by top ML practitioners including teams at NVIDIA, OpenAI, Lyft, Blue River Technology, Toyota, and MILA, Weights & Biases is part of the new standard of best practices for machine learning.

Contact:
PR@wandb.com

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Wed, 30 Nov 2022 23:01:00 -0600 text/html https://www.benzinga.com/pressreleases/22/12/n29923419/weights-and-biases-joins-the-amazon-sagemaker-ready-program
Killexams : AWS Announces Eight New Amazon SageMaker Capabilities

Amazon SageMaker Role Manager makes it easier for administrators to control access and define permissions for improved machine learning governance

Amazon SageMaker Model Cards make it easier to document and review model information throughout the machine learning lifecycle

Amazon SageMaker Model Dashboard provides a central interface to track models, monitor performance, and review historical behavior

New data preparation capability in Amazon SageMaker Studio Notebooks helps customers visually inspect and address data-quality issues in a few clicks

Data science teams can now collaborate in real time within Amazon SageMaker Studio Notebook

Customers can now automatically convert notebook code into production-ready jobs

Automated model validation enables customers to test new models using real-time inference requests

Support for geospatial data enables customers to more easily develop machine learning models for climate science, urban planning, disaster response, retail planning, precision agriculture, and more

LAS VEGAS, November 30, 2022--(BUSINESS WIRE)--At AWS re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), today announced eight new capabilities for Amazon SageMaker, its end-to-end machine learning (ML) service. Developers, data scientists, and business analysts use Amazon SageMaker to build, train, and deploy ML models quickly and easily using its fully managed infrastructure, tools, and workflows. As customers continue to innovate using ML, they are creating more models than ever before and need advanced capabilities to efficiently manage model development, usage, and performance. Today’s announcement includes new Amazon SageMaker governance capabilities that provide visibility into model performance throughout the ML lifecycle. New Amazon SageMaker Studio Notebook capabilities provide an enhanced notebook experience that enables customers to inspect and address data-quality issues in just a few clicks, facilitate real-time collaboration across data science teams, and accelerate the process of going from experimentation to production by converting notebook code into automated jobs. Finally, new capabilities within Amazon SageMaker automate model validation and make it easier to work with geospatial data. To get started with Amazon SageMaker, visit aws.amazon.com/sagemaker.

"Today, tens of thousands of customers of all sizes and across industries rely on Amazon SageMaker. AWS customers are building millions of models, training models with billions of parameters, and generating trillions of predictions every month. Many customers are using ML at a scale that was unheard of just a few years ago," said Bratin Saha, vice president of Artificial Intelligence and Machine Learning at AWS. "The new Amazon SageMaker capabilities announced today make it even easier for teams to expedite the end-to-end development and deployment of ML models. From purpose-built governance tools to a next-generation notebook experience and streamlined model testing to enhanced support for geospatial data, we are building on Amazon SageMaker’s success to help customers take advantage of ML at scale."

The cloud enabled access to ML for more users, but until a few years ago, the process of building, training, and deploying models remained painstaking and tedious, requiring continuous iteration by small teams of data scientists for weeks or months before a model was production-ready. Amazon SageMaker launched five years ago to address these challenges, and since then AWS has added more than 250 new features and capabilities to make it easier for customers to use ML across their businesses. Today, some customers employ hundreds of practitioners who use Amazon SageMaker to make predictions that help solve the toughest challenges around improving customer experience, optimizing business processes, and accelerating the development of new products and services. As ML adoption has increased, so have the types of data that customers want to use, as well as the levels of governance, automation, and quality assurance that customers need to support the responsible use of ML. Today's announcement builds on Amazon SageMaker's history of innovation in supporting practitioners of all skill levels, worldwide.

New ML governance capabilities in Amazon SageMaker

Amazon SageMaker offers new capabilities that help customers more easily scale governance across the ML model lifecycle. As the number of models and users within an organization increases, it becomes harder to set least-privilege access controls and establish governance processes to document model information (e.g., input data sets, training environment information, model-use description, and risk rating). Once models are deployed, customers also need to monitor for bias and feature drift to ensure they perform as expected.

  • Amazon SageMaker Role Manager makes it easier to control access and permissions: Appropriate user-access controls are a cornerstone of governance and support data privacy, prevent information leaks, and ensure practitioners can access the tools they need to do their jobs. Implementing these controls becomes increasingly complex as data science teams swell to dozens or even hundreds of people. ML administrators—individuals who create and monitor an organization’s ML systems—must balance the push to streamline development while controlling access to tasks, resources, and data within ML workflows. Today, administrators create spreadsheets or use ad hoc lists to navigate access policies needed for dozens of different activities (e.g., data prep and training) and roles (e.g., ML engineer and data scientist). Maintaining these tools is manual, and it can take weeks to determine the specific tasks new users will need to do their jobs effectively. Amazon SageMaker Role Manager makes it easier for administrators to control access and define permissions for users. Administrators can select and edit prebuilt templates based on various user roles and responsibilities. The tool then automatically creates the access policies with necessary permissions within minutes, reducing the time and effort to onboard and manage users over time.

  • Amazon SageMaker Model Cards simplify model information gathering: Today, most practitioners rely on disparate tools (e.g., email, spreadsheets, and text files) to document the business requirements, key decisions, and observations during model development and evaluation. Practitioners need this information to support approval workflows, registration, audits, customer inquiries, and monitoring, but it can take months to gather these details for each model. Some practitioners try to solve this by building complex recordkeeping systems, which is manual, time consuming, and error-prone. Amazon SageMaker Model Cards provide a single location to store model information in the AWS console, streamlining documentation throughout a model’s lifecycle. The new capability auto-populates training details like input datasets, training environment, and training results directly into Amazon SageMaker Model Cards. Practitioners can also include additional information using a self-guided questionnaire to document model information (e.g., performance goals, risk rating), training and evaluation results (e.g., bias or accuracy measurements), and observations for future reference to further Boost governance and support the responsible use of ML.

  • Amazon SageMaker Model Dashboard provides a central interface to track ML models: Once a model has been deployed to production, practitioners want to track their model over time to understand how it performs and to identify potential issues. This task is normally done on an individual basis for each model, but as an organization starts to deploy thousands of models, this becomes increasingly complex and requires more time and resources. Amazon SageMaker Model Dashboard provides a comprehensive overview of deployed models and endpoints, enabling practitioners to track resources and model behavior in one place. From the dashboard, customers can also use built-in integrations with Amazon SageMaker Model Monitor (AWS’s model and data drift monitoring capability) and Amazon SageMaker Clarify (AWS’s ML bias-detection capability). This end-to-end visibility into model behavior and performance provides the necessary information to streamline ML governance processes and quickly troubleshoot model issues.

To learn more about Amazon SageMaker governance capabilities, visit aws.amazon.com/sagemaker/ml-governance.

Next-generation Notebooks

Amazon SageMaker Studio Notebook gives practitioners a fully managed notebook experience, from data exploration to deployment. As teams grow in size and complexity, dozens of practitioners may need to collaboratively develop models using notebooks. AWS continues to offer the best notebook experience for users with the launch of three new features that help customers coordinate and automate their notebook code.

  • Simplified data preparation: Practitioners want to explore datasets directly in notebooks to spot and correct potential data-quality issues (e.g., missing information, extreme values, skewed datasets, and biases) as they prepare data for training. Practitioners can spend months writing boilerplate code to visualize and examine different parts of their dataset to identify and fix problems. Amazon SageMaker Studio Notebook now offers a built-in data preparation capability that allows practitioners to visually review data characteristics and remediate data-quality problems in just a few clicks—all directly in their notebook environment. When users display a data frame (i.e., a tabular representation of data) in their notebook, Amazon SageMaker Studio Notebook automatically generates charts to help users identify data-quality issues and suggests data transformations to help fix common problems. Once the practitioner selects a data transformation, Amazon SageMaker Studio Notebook generates the corresponding code within the notebook so it can be repeatedly applied every time the notebook is run.

  • Accelerate collaboration across data science teams: After data has been prepared, practitioners are ready to start developing a model—an iterative process that may require teammates to collaborate within a single notebook. Today, teams must exchange notebooks and other assets (e.g., models and datasets) over email or chat applications to work on a notebook together in real time, leading to communication fatigue, delayed feedback loops, and version-control issues. Amazon SageMaker now gives teams a workspace where they can read, edit, and run notebooks together in real time to streamline collaboration and communication. Teammates can review notebook results together to immediately understand how a model performs, without passing information back and forth. With built-in support for services like BitBucket and AWS CodeCommit, teams can easily manage different notebook versions and compare changes over time. Affiliated resources, like experiments and ML models, are also automatically saved to help teams stay organized.

  • Automatic conversion of notebook code to production-ready jobs: When practitioners want to move a finished ML model into production, they usually copy snippets of code from the notebook into a script, package the script with all its dependencies into a container, and schedule the container to run. To run this job repeatedly on a schedule, they must set up, configure, and manage a continuous integration and continuous delivery (CI/CD) pipeline to automate their deployments. It can take weeks to get all the necessary infrastructure set up, which takes time away from core ML development activities. Amazon SageMaker Studio Notebook now allows practitioners to select a notebook and automate it as a job that can run in a production environment. Once a notebook is selected, Amazon SageMaker Studio Notebook takes a snapshot of the entire notebook, packages its dependencies in a container, builds the infrastructure, runs the notebook as an automated job on a schedule set by the practitioner, and deprovisions the infrastructure upon job completion, reducing the time it takes to move a notebook to production from weeks to hours.

To begin using the next generation of Amazon SageMaker Studio Notebooks and these new capabilities, visit aws.amazon.com/sagemaker/notebooks.

Automated validation of new models using real-time inference requests

Before deploying to production, practitioners test and validate every model to check performance and identify errors that could negatively impact the business. Typically, they use historical inference request data to test the performance of a new model, but this data sometimes fails to account for current, real-world inference requests. For example, historical data for an ML model to plan the fastest route might fail to account for an accident or a sudden road closure that significantly alters the flow of traffic. To address this issue, practitioners route a copy of the inference requests going to a production model to the new model they want to test. It can take weeks to build this testing infrastructure, mirror inference requests, and compare how models perform across key metrics (e.g., latency and throughput). While this provides practitioners with greater confidence in how the model will perform, the cost and complexity of implementing these solutions for hundreds or thousands of models makes it unscalable.

Amazon SageMaker Inference now provides a capability to make it easier for practitioners to compare the performance of new models against production models, using the same real-world inference request data in real time. Now, they can easily scale their testing to thousands of new models simultaneously, without building their own testing infrastructure. To start, a customer selects the production model they want to test against, and Amazon SageMaker Inference deploys the new model to a hosting environment with the exact same conditions. Amazon SageMaker routes a copy of the inference requests received by the production model to the new model and creates a dashboard to display performance differences across key metrics, so customers can see how each model differs in real time. Once the customer validates the new model’s performance and is confident it is free of potential errors, they can safely deploy it. To learn more about Amazon SageMaker Inference, visit aws.amazon.com/sagemaker/shadow-testing.

New geospatial capabilities in Amazon SageMaker make it easier for customers to make predictions using satellite and location data

Today, most data captured has geospatial information (e.g., location coordinates, weather maps, and traffic data). However, only a small amount of it is used for ML purposes because geospatial datasets are difficult to work with and can often be petabytes in size, spanning entire cities or hundreds of acres of land. To start building a geospatial model, customers typically augment their proprietary data by procuring third-party data sources like satellite imagery or map data. Practitioners need to combine this data, prepare it for training, and then write code to divide datasets into manageable subsets due to the massive size of geospatial data. Once customers are ready to deploy their trained models, they must write more code to recombine multiple datasets to correlate the data and ML model predictions. To extract predictions from a finished model, practitioners then need to spend days using open source visualization tools to render on a map. The entire process from data enrichment to visualization can take months, which makes it hard for customers to take advantage of geospatial data and generate timely ML predictions.

Amazon SageMaker now accelerates and simplifies generating geospatial ML predictions by enabling customers to enrich their datasets, train geospatial models, and visualize the results in hours instead of months. With just a few clicks or using an API, customers can use Amazon SageMaker to access a range of geospatial data sources from AWS (e.g., Amazon Location Service), open-source datasets (e.g., Amazon Open Data), or their own proprietary data including from third-party providers (like Planet Labs). Once a practitioner has selected the datasets they want to use, they can take advantage of built-in operators to combine these datasets with their own proprietary data. To speed up model development, Amazon SageMaker provides access to pre-trained deep-learning models for use cases such as increasing crop yields with precision agriculture, monitoring areas after natural disasters, and improving urban planning. After training, the built-in visualization tool displays data on a map to uncover new predictions. To learn more about Amazon SageMaker’s new geospatial capability, visit aws.amazon.com/sagemaker/geospatial.

Capitec Bank is South Africa's largest digital bank with over 10 million digital clients. "At Capitec, we have a wide range of data scientists across our product lines who build differing ML solutions," said Dean Matter, ML engineer at Capitec Bank. "Our ML engineers manage a centralized modeling platform built on Amazon SageMaker to empower the development and deployment of all of these ML solutions. Without any built-in tools, tracking modelling efforts tends toward disjointed documentation and a lack of model visibility. With Amazon SageMaker Model Cards, we can track plenty of model metadata in a unified environment, and Amazon SageMaker Model Dashboard provides visibility into the performance of each model. In addition, Amazon SageMaker Role Manager simplifies access management for data scientists in our different product lines. Each of these contribute toward our model governance being sufficient to warrant the trust that our clients place in us as a financial services provider."

EarthOptics is a soil-data-measurement and mapping company that leverages proprietary sensor technology and data analytics to precisely measure the health and structure of soil. "We wanted to use ML to help customers increase agricultural yields with cost-effective soil maps," said Lars Dyrud, CEO of EarthOptics. "Amazon SageMaker’s geospatial ML capabilities allowed us to rapidly prototype algorithms with multiple data sources and reduce the amount of time between research and production API deployment to just a month. Thanks to Amazon SageMaker, we now have geospatial solutions for soil carbon sequestration deployed for farms and ranches across the U.S."

HERE Technologies is a leading location-data and technology platform that helps customers create custom maps and location experiences built on highly precise location data. "Our customers need real-time context as they make business decisions leveraging insights from spatial patterns and trends," said Giovanni Lanfranchi, chief product and technology officer for HERE Technologies. "We rely on ML to automate the ingestion of location-based data from varied sources to enrich it with context and accelerate analysis. Amazon SageMaker’s new testing capabilities allowed us to more rigorously and proactively test ML models in production and avoid adverse customer impact and any potential outages because of an error in deployed models. This is critical, since our customers rely on us to provide timely insights based on real-time location data that changes every minute."

Intuit is the global financial technology platform that powers prosperity for more than 100 million customers worldwide with TurboTax, Credit Karma, QuickBooks, and Mailchimp. "We’re unleashing the power of data to transform the world of consumer, self-employed, and small business finances on our platform," said Brett Hollman, director of Engineering and Product Development at Intuit. "To further Boost team efficiencies for getting AI-driven products to market with speed, we've worked closely with AWS in designing the new team-based collaboration capabilities of SageMaker Studio Notebooks. We’re excited to streamline communication and collaboration to enable our teams to scale ML development with Amazon SageMaker Studio."

About Amazon Web Services

For over 15 years, Amazon Web Services has been the world’s most comprehensive and broadly adopted cloud offering. AWS has been continually expanding its services to support virtually any cloud workload, and it now has more than 200 fully featured services for compute, storage, databases, networking, analytics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, virtual and augmented reality (VR and AR), media, and application development, deployment, and management from 96 Availability Zones within 30 geographic regions, with announced plans for 15 more Availability Zones and five more AWS Regions in Australia, Canada, Israel, New Zealand, and Thailand. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—trust AWS to power their infrastructure, become more agile, and lower costs. To learn more about AWS, visit aws.amazon.com.

About Amazon

Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. Amazon strives to be Earth’s Most Customer-Centric Company, Earth’s Best Employer, and Earth’s Safest Place to Work. Customer reviews, 1-Click shopping, personalized recommendations, Prime, Fulfillment by Amazon, AWS, Kindle Direct Publishing, Kindle, Career Choice, Fire tablets, Fire TV, Amazon Echo, Alexa, Just Walk Out technology, Amazon Studios, and The Climate Pledge are some of the things pioneered by Amazon. For more information, visit amazon.com/about and follow @AmazonNews.

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Wed, 30 Nov 2022 05:16:00 -0600 en-NZ text/html https://nz.finance.yahoo.com/news/aws-announces-eight-amazon-sagemaker-191600065.html
Killexams : Upsolver Announces Support for AWS for Advertising and Marketing Initiative

SAN FRANCISCO--(BUSINESS WIRE)--Nov 30, 2022--

Upsolver, the company dedicated to making data in motion accessible to every data practitioner, announced support for the AWS for Advertising and Marketing initiative from Amazon Web Services (AWS) to help accelerate advertising and marketing transformation.

AWS for Advertising and Marketing is an initiative featuring services and solutions purpose-built to meet the needs of advertising agencies, marketers, publishers, ad technology providers, and analytics service providers. The initiative helps customers deliver personalized ad experiences, optimize ad serving performance and cost, and innovate on audience segmentation and attribution. It simplifies the process for industry customers to select the right tools and partners helping accelerate their production launches and see faster time to value.

Upsolver SQLake is a platform for building data pipelines that ingest and combine real-time events with batch data sources for up-to-the-minute analytics. It provides ground-breaking time-to-value, since any SQL user can build a pipeline simply by writing a query. SQLake automates the pipeline engineering tasks that create severe development bottlenecks – chores such as orchestration, file system optimization and infrastructure scaling.

AWS empowers advertisers and marketers to reinvent workloads with solutions to Boost audience and customer data management, privacy-enhanced data collaboration, advertising platforms, marketing measurement and ad intelligence, and personalized digital customer experiences. For customers looking for prescriptive, solution-specific support, AWS for Advertising & Marketing identifies leading industry partners in each area.

With SQLake, advertisers and marketers achieve a leap in time to delivery and data freshness for use cases such as machine learning (ML) model training, campaign performance management and optimization, dynamic audience segmentation, real-time bidding, ROI reporting, data science and ad hoc analytics. Beyond making data engineers 10X more productive it enables self-service for data users who know SQL – such as data scientists, analysts, ad/marketing ops personnel, product managers and account managers.

One benefit of migrating or building advertising and marketing workloads on the most widely adopted cloud is the number of integrations and distribution channels connecting shared data with flexibility and interoperability. Whether you are seeking third party data or tools for better managing first party data, there are both AWS and third-party solutions offered in the AWS Data Exchange, AWS Marketplace, along with the largest community of AWS Partner Network (APN) members, including Upsolver.

“As a global leader in smart ad serving, omnichannel personalization, and consumer intelligence, Clinch ingests and processes billions of events per day into our AWS data lake, a scenario that is largely supported by Upsolver," said Yaron Cohen, Vice President of Research and Development at Clinch. "The self-serve, intuitive operability offered by Upsolver has driven an immense amount of efficiency and speed to my team, and enables us to deliver new features quickly that Boost ROI for our customers.”

Together, Upsolver and AWS help serve the analytics needs of advertising and marketing firms such as AppsFlyer, SimilarWeb, Clinch, Peer39, Mantis, BigaBid and MediaSense.

Support for AWS Redshift Serverless is Upsolver’s latest advancement in supporting AWS customers, who can use Upsolver with a broad range of services including Amazon Simple Storage Service (Amazon S3), Amazon Athena, Amazon Kinesis, Amazon Managed Streaming for Apache Kafka (Amazon MSK), Amazon Redshift, Amazon SageMaker and more.

Sign up for a 30-day risk-free trial of SQLake today, after which usage is charged at 10 cents / GB of data ingested, with unlimited pipelines and no minimum commit. Upsolver is available for subscription on AWS Marketplace.

About Upsolver

Upsolver is a tight-knit group of data engineers and infrastructure developers obsessed with removing the friction from building data pipelines, in order to accelerate the real-time delivery of big data to the people who need it.

Founded in 2015 by data engineers Ori Rafael and Yoni Eini, Upsolver has grown from an Israeli-based venture focused on adtech to a global business serving customers across many industries including software, manufacturing, oil and gas, health care, and financial services. Upsolver’s platform enables a variety of high-value analytics use cases such as user behavior, IoT monitoring, and log analytics.

Upsolver is headquartered in San Francisco with R&D centered in Tel Aviv. Customers span regions and industries, such as Cox Automotive, IronSource, ProofPoint and Wix. Its top-tier investors include Scale Venture Partners, Vertex Ventures US, Wing Venture Capital, and JVP. For more information, please visit www.upsolver.com.

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Killexams : AWS aims to future-proof enterprise data strategy with a slew of new database and analytics tools

After introducing a number of new services for machine learning and data analytics on Tuesday, Amazon Web Services Inc. doubled down today with a host of new tools aimed at providing what one top executive described as a “future-proof” data strategy.

In a keynote address led by Swami Sivasubramanian, vice president of database, analytics and machine learning at AWS, the cloud giant introduced enhancements for file ingestion, workload scaling and managing overall data quality. Sivasubramanian noted at the start of his presentation that the latest innovations were a continuation of Amazon’s lengthy history of data management.

“We have been in the data business long before AWS came into existence,” Sivasubramanian said. “We used data to anticipate our customers’ need for expanded storage which paved the way for AWS. We built our business on data.”

New analytics capabilities

To help other enterprises build on data, AWS focused this week on providing tools designed to address a number of enterprise pain points by making it faster and easier to manage and analyze data at petabyte scale. The company unveiled several new database and analytics capabilities today to support this approach.

Amazon OpenSearch Serverless will help run search and analytics workloads without requiring the configuration or management of underlying infrastructure. Workload support also got a boost through the introduction of Amazon DocumentDB Elastic Clusters to scale customer document workloads to support millions of writes per second and multi-petabyte storage of data.

AWS also rolled out Amazon Athena for Apache Spark. This new feature will reduce the time to use Spark for interactive analytics from minutes to less than a second, according to Sivasubramanian.

Building on its AWS Glue serverless data integration service, the company announced Glue Data Quality which will reduce time for data analysis by automatically measuring and monitoring data quality across pipelines. And the Amazon Redshift cloud data warehouse will now support high-availability configuration across multiple AWS Availability Zones. The goal is to deliver high reliability and availability to support mission critical analytics workloads, balancing data security with the need for faster recovery.

“While these security solutions are critical, we also believe they should not slow you down,” Sivasubramanian said.

In addition to the availability zone enhancement for Redshift, AWS announced Centralized Access Controls for Redshift Data Sharing which governs access using AWS Lake Formation, and a new ability to auto-copy files into Redshift from S3.

SageMaker updates

Amazon SageMaker, the company’s cloud machine learning platform, was clearly a focus this week. AWS unveiled eight new SageMaker capabilities today, including a Model Dashboard for tracking machine learning model performance, a Role Manager solution for defining access and permissions, and a streamlined data preparation capability in SageMaker Studio Notebooks.

The company also showcased an expanded capability for SageMaker in supporting the use of geospatial data for customers. SageMaker will now simplify the generation of geospatial machine learning predictions and speed up model development.

“Geospatial datasets are typically massive and unstructured, and the tools are really limited,” Sivasubramanian said. “We are making it easier for customers to unlock the value of geospatial data. These types of innovations demonstrate the impact that data can have on organizations and the world.”

In addition, AWS announced general availability of Trusted Language Extensions for PostgreSQL, a new open-source development kit. The company already supported more than 85 PostgreSQL extensions in Amazon Aurora and Amazon RDS, and this latest release was in response to customers’ interest in flexibility to build and run their own extensions for PostgreSQL database instances.

Aurora also received additional support through the addition of Amazon GuardDuty RDS Protection using machine learning to identify threats to data stored in Aurora databases. This single-click functionality will be available to AWS customers at no additional cost during the preview period.

“ML and AI is all about the data,” CloudFix Chief Executive Rahul Subramaniam told SiliconANGLE. ” Over the last five years, AWS has spent time and effort convincing practitioners that AWS was the place to execute data driven innovation — and I believe they have been very successful at executing that. The keynote today was leaning in on convincing the C-suite and legal teams that AWS has all the governance tools and practices in place for them to feel comfortable with letting their data reside in AWS and unlocking the innovation that can come from it.”

Sivasubramanian outlined a vision in his keynote of an enterprise world in which data becomes the connective tissue threaded across organizations. This will create a lasting culture of innovation, according to the AWS executive, built around data and the tools to maximize its value.

“It’s individuals who create these sparks of innovations, but it is the leaders who must create a data driven culture to help them get there,” he said.

Photo: Robert Hof/SiliconANGLE

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Killexams : ThunderSoft Joins the Amazon SageMaker Ready Program No result found, try new keyword!Joining the Amazon SageMaker Ready Program differentiates ThunderSoft as an AWS Partner Network (APN) member with a product that works with Amazon SageMaker and is generally available for and fully ... Tue, 29 Nov 2022 10:00:00 -0600 en-US text/html https://www.tmcnet.com/usubmit/-thundersoft-joins-amazon-sagemaker-ready-program-/2022/11/30/9722485.htm Killexams : Wage growth: San Antonio jobs with greatest pay increases during COVID Shipment loaders like this Amazon Fullfillment Center worker in Schertz, TX, saw a 16% increase in wage between 2018 and 2021, according to statewide Texas data from the Bureau of Labor Statistics. © Bob Owen, Staff / San Antonio Express-News

Shipment loaders like this Amazon Fullfillment Center worker in Schertz, TX, saw a 16% increase in wage between 2018 and 2021, according to statewide Texas data from the Bureau of Labor Statistics.

So much about how we live and work has changed since the pandemic came crashing through the world three years ago. More people are working from home. Labor shortages across an array of industries have caused companies and business owners to increase wages or risk losing employees. We now buy our toilet paper in bulk, through the mail.

An Express-News analysis of data from the Bureau of Labor Statistics can help shine a more focused light on how specific jobs have changed in terms of employment and wages within the San Antonio metro area. 

Occupations with the biggest wage increase

Across the nation, the pandemic led to an increase in drug abuse and fatal overdoses. Statewide, Texas saw a 30% increase in drug overdose deaths between 2019 and 2020. In San Antonio, community and social service occupations saw, on average, a 16.2% increase in annual pay, the largest of all occupation groups between 2018 and 2021. This group of workers includes mental health and substance abuse social workers, religious education directors and probation officers.

The pandemic also brought about a world where everything from weekly groceries to mattresses are delivered to our doors. E-commerce saw a 43% increase across the U.S. as travel bans and other COVID restrictions on public interaction spurred greater spending online.

The occupation group with the second highest wage increase for the San Antonio area goes to Transportation and Material Moving Occupations which includes stockers and order fillers (i.e. Amazon warehouse workers) and delivery persons (i.e. FedEx drivers). Workers in this occupation group saw, on average, a 14.4% wage increase.

Healthcare Practitioners and Technical Occupations

The pandemic has taken a toll on medical workers, especially in high transmission areas like San Antonio. On average, San Antonio workers in the healthcare practitioners occupation group saw a 9.9% increase in wages between 2018 and 2021. This group includes some of the highest paid occupations in the city, like psychiatrists, obstetricians and gynecologists and family medicine physicians. 

Food Preparation and Serving Related Occupations

Food service jobs have been in the limelight since the pandemic began, both due to labor shortages and a renewed focus on the low wages earned by those workers. On average, San Antonio workers in this group saw an 11.5% increase in wages between 2018 and 2021. This group includes some of the lowest paid occupations in the city, like restaurant hosts, short-order cooks and barbacks. 

More about the data

In total, of the 620 detailed occupations available for the San Antonio metro area:

  • 376 occupations saw wage increases
  • 104 saw wage decreases
  • 140 were not comparable across the time period

Each year, the BLS releases the Occupational Employment and Wage Statistics dataset which looks at wages and employment by occupation type. With this data we can see that the top San Antonio occupations in terms of number of workers are Customer Service Representatives and Retail Salespersons.

This data is unique because it deals with the jobs people do, not the industries in which they work. This is an important distinction because through them, we can learn about people, rather than just companies.

Each annual release is constructed from data points gathered over the past 3 years, allowing the BLS more information to provide more accurate estimates of detailed occupations at the metro level.

Because the BLS uses data from past years to construct their annual wage and employment dataset, effects to the market from situations like COVID may take a while to show up. The 2021 release contains data points from November 2018 to May 2021, a mix of COVID and non-COVID information.

Additionally, definitions of what an occupation represents may change over time. For example, in the 2018 dataset, “Web Developers” covered workers who both designed and developed websites. As of 2021, developers and designers were split into two distinct groups of workers. 

In order to account for these potential incongruities, the Express-News did not analyze occupations that underwent substantial definition or scope changes. However, additions and subtractions of occupations between years may still affect some of the occupations analyzed in this article.

Still, the data paint an interesting picture of how wages for different occupations have changed since COVID up-ended society.

Mon, 05 Dec 2022 05:26:02 -0600 en-US text/html https://www.msn.com/en-us/money/careersandeducation/these-san-antonio-jobs-saw-their-pay-increase-the-most-during-covid/ar-AA14W8f0
Killexams : AWS updates machine learning service SageMaker

Amazon Web Services (AWS) has added new features to its managed machine learning service Amazon SageMaker, designed to Boost governance attributes within the service and adding new capabilities to its notebooks.

Notebooks in context of Amazon SageMaker are compute instances that run the Jupyter Notebook application.

Governance updates to Boost granular access, Boost workflow

AWS said the new features will allow enterprises to scale governance across their ML model lifecycle. As the number of machine learning models increases, it can get challenging for enterprises to manage the task of setting privilege access controls and establishing governance processes to document model information, such as input data sets, training environment information, model-use description, and risk rating.

Data engineering and machine learning teams currently use spreadsheets or ad hoc lists to navigate access policies needed for all processes involved. This can become complex as the size of machine learning teams increases within an enterprise, AWS said in a statement.

Another challenge is to monitor the deployed models for bias and ensure they are performing as expected, the vendor said.

To tackle these challenges, the cloud services provider has added Amazon SageMaker Role Manager to make it easier for administrators to control access and define permission for users.

With the new tool, administrators can select and edit prebuilt templates based on various user roles and responsibilities. The tool then automatically creates access policies with necessary permissions within minutes, the company said.

AWS has also added a new tool to SageMaker called Amazon SageMaker Model Cards to help data science teams shift from manual record keeping.

The tool provides a single location to store model information in the AWS console and it can auto-populate training details like input data sets, training environment, and training results directly into Amazon SageMaker Model Cards, the company said.