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C1000-024 syllabus - IBM Grid Scale Cloud Storage V2 Updated: 2023

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Exam Code: C1000-024 IBM Grid Scale Cloud Storage V2 syllabus June 2023 by Killexams.com team
IBM Grid Scale Cloud Storage V2
IBM Storage syllabus

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IBM Grid Scale Cloud Storage V2
Question: 73
A storage administrator has a storage requirement of a non-HDD storage capacity that would be scalable up to 80 TB
of internal storage. It must also have several host connectivity options such as Fibre Channel, ISCSI, and FCoE.
Which storage device meets the customers requirement?
A . DCS3860
B . IBM ProtecTIER
C . IBM Flash System V840
D . IBM SAN Volume Controller
Answer: C
Question: 74
An IBM customer is considering more spinning disk for a new business analytics application. The IBM specialist is
requested to provide information as to challenges that the customer might face and what other customers in the
marketplace have implemented.
Which factor should provide best performance?
A . Implementation of flash technology
B . Implementation of solid state disk drives
C . Move of most accessed files to its own disk
D . Increase the number of disks in storage array
Answer: A
Question: 75
A customer currently has an IBM Storwize V7000 storage environment and would like to have a view of its SAN,
additional replication features, and a deeper analytics of its Mdisks
Which product should the technical specialist demonstrate?
A . IBM Spectrum Scale
B . IBM Spectrum Protect
C . IBM Spectrum Control
D . IBM Spectrum Archive
Answer: C
Question: 76
When considering the addition of FCoE into an existing Ethernet environment, what must be enabled to reuse the
B . FC frames
C . Jumbo frames
D . FC transport protocol
Answer: C
Question: 77
A customer has storage systems from HP and EMC connected on its Fibre Channel network. They are for separate
departments and the customer is considering a new system for the financial department and a consolidation of the
existing storage systems.
Which feature should the storage specialist emphasize on IBM Storwize V7000 to address this concern?
A . Easy Tier
B . Thin provisioning
C . External virtualization
D . Real-time Compression
Answer: C
Question: 78
A customer has been experiencing sporadic performance issues on its IBM Storwize V7000 system. Analysis has
shown that during peak workloads it is CPU constrained.
What can be done to alleviate the CPU contention?
A . Add a second I/O group
B . Add IBM Spectrum Control
C . Perform a Capacity Magic study
D . Implement Real-time Compression
Answer: A
Question: 79
A customer currently has a NetApp solution today and is unhappy with the performance of the system. The customer is
very happy with the SnapManager software from NetApp, which is one of the challenges for moving the customer to a
new IBM storage product.
What additional product should the technical specialist show the customer to help ease its concern about utilizing
A . IBM Spectrum Protect
B . Tivoli Storage Productivity Center
C . Tivoli Storage FlashCopy Manager
D . TotalStorage Productivity Center for Replication
Answer: C
Question: 80
A customer has two data centers located 15 kilometers apart. One site is for production and the other is a DR hot site.
In an effort to maximize the life span of the storage subsystems, equipment retired at the production site is put into
service at the hot site. This has led to a heterogeneous storage environment across both locations and complexity in
keeping the data synchronized and uncorrupted.
Which aspect of virtualization within the Storwize family should be emphasized by the pre-sales storage person to
enable this disaster recovery plan?
A . A single set of advanced copy services
B . Consolidation to a single pool of storage
C . Ease transition to an on-demand IT infrastructure
D . Significantly reduced planned and unplanned downtime
Answer: A
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IBM Storage syllabus - BingNews https://killexams.com/pass4sure/exam-detail/C1000-024 Search results IBM Storage syllabus - BingNews https://killexams.com/pass4sure/exam-detail/C1000-024 https://killexams.com/exam_list/IBM Course Syllabus Information

In addition to the eleven required components listed above, many instructors also find it useful to include information about or guidance on a range of other topics. The following list is drawn from common practices at SLU, as well as from the literature on effective syllabus construction and on creating inclusive courses that support student learning and success. This list is by no means exhaustive or in order of priority. Note: for some academic units, items on this list also may be required. Click here for a printer-friendly version.

Additional course information:

  • An expanded description of the course, its priorities, key concepts, etc.
  • Course schedule with due dates for assignments, exams, reading, and other activities
  • Disclaimer about the possibility of changes to the course schedule 

Additional instructor information:

  • Instructor office location and office hours

Additional information about learning activities /assignments:

  • Description of informal learning activities students will engage in (e.g., informal in-class activities, participation expectations, service-learning experiences)
  • Articulation of the link between course assignments/activities and stated learning outcomes, objectives, and/or competencies 

Additional information about course materials:

  • Recommended and/or optional readings or texts
  • Information about accessing electronic reserves

Additional information about student support resources:

  • University-wide academic success and support resources

-- Insert and/or link to recommended text for the Student Success Center here

-- Insert and/or link to recommended text for University Writing Services here

-- Insert and/or link to recommended text for the University Counseling Center here.

  • Course-/program-specific support resources [if applicable]
  • Other campus resources relevant to the course (e.g., liaison librarian, residence hall coordinator for learning community courses, etc.) 

Additional information about academic honesty:

  • Unit-level academic honesty policies and practices [if applicable]
  • Course-specific guidance on academic honesty
  • Statements of professional ethics or codes of conduct [if applicable] 

Other information:

  • Basic needs syllabus security statement (like this one, which was developed at SLU to alert students to campus resources for things like food and shelter insecurity)
  • Course etiquette/civility policies or other expectations about interactions between and among members of the class
    • With a significant number of SLU courses now being conducted via various distance education modalities, a University-wide recommended syllabus statement on distance education etiquette is warranted. This statement is recommended for all syllabi for all courses at all locations (except the Madrid Campus) offered by the colleges/schools and other academic units reporting to the University Provost.
  • Information about what will happen in cases of inclement weather
  • Information about relevant safety/security protocols and procedures (e.g., location of eye wash stations; active shooter response, etc.)
  • Distinction between “excused” and “unexcused” absences [if applicable and consistent with University attendance policy]
  • Statement that student work in the course may be used in course/program assessment
  • Information about requirements for experiential/off-campus learning (e.g., liability waiver, background check, internship learning contract, service expectations, etc.)
Thu, 25 Nov 2021 15:41:00 -0600 en text/html https://www.slu.edu/provost/faculty-affairs/teaching-resources-for-faculty/course-syllabus-information/index.php
IBM to Showcase Open Analytics Push at PrestoCon Day


The PrestoDB community will come together this Wednesday for PrestoCon Day, the third annual virtual event showcasing the popular open source SQL engine. Representatives from Uber, Adobe, Alibaba, and TikTok will share stories about how they use PrestoDB and open analytics in general. One vendor looking to make a splash is IBM, which is the new owner of an enterprise PrestoDB offering and the latest adherent to open lakehouse architectures.

Thanks to its consistently high performance on batch and interactive workloads, ability to scale linearly, and adherence to ANSI SQL standards, PrestoDB has become one of the most popular open query engines available today. The software was originally developed at Facebook as the successor to Apache Hive during the Hadoop heyday, but today PrestoDB is readily used on a wide range of big data repositories, including relational databases, object storage stores, and distributed file systems.

PrestoDB naturally will be the star attraction at PrestoCon Day, a one-day virtual event that is free to attend. The fun starts at 8:30 a.m. PT, when Presto Foundation members Ali LeClerc and Girish Baliga welcome the community together and deliver their opening remarks. More than 20 sessions follow, ranging from case studies on PrestoDB usage at Bytedance and Alibaba Cloud to discussions on the latest Presto features, such as Intel’s contribution to Project Velox to how PrestoDB fits into HPE’s Ezmeral lineup.

IBM will also attend the virtual event. While Big Blue is a longtime purveyor of proprietary software and systems, it is now in the midst of a full-scale embrace of open analytics and open platforms, such as PrestoDB and data lakehouse architectures.

IBM is the latest member of the Presto Foundation, the governing body behind PrestoDB. Thanks to its acquisition of PrestoDB vendor Ahana in April, the company joined a seat on the board of the Presto Foundation, which is a part of the Linux Foundation.

You can sign up for PrestoCon Day here

Presto fits neatly into IBM’s new lakehouse offering, called Watsonx.data, which it unveiled in May. Lakehouses have grown in popularity as a happy medium between data lakes such as Hadoop, which had a habit of turning into ungoverned but super-scalable data free-for-alls, and data warehouses, which delivered good data reliability and governance but carried extra cost and had limited scalability.

Compared to IBM’s previous forays into big data and Hadoop, the new generation of open analytics technologies, as personified by Presto, is much more ready for prime-time, says Vikram Murali, who is the vice president of software development for data and AI at IBM.

“I truly believe we are at a point where, when we GA this thing, customers will see that we have solved a lot of these issues,” Murali says. “And that is one of the reasons, by the way, that we chose Presto. We could have chosen to go down the route of creating another proprietary engine. But instead, we wanted to go with something that was available in open source, something that was mature, where companies like Uber and Meta have been using it for years, and they have already solved the scalability [and] the elasticity [issues]. So all of those have become table stakes now, and that’s what we gain by going with Presto.”

IBM plans to make its Watsonx.data lakehouse general availability next month. The plan calls for launching two fully managed Watsonx.data lakehouse offerings, including one on AWS that uses S3 storage (basically the pre-existing managed offering from Ahana) and another on IBM Cloud that uses S3-compatable storage from IBM’s Cloud Object Store (COS).

Users can also deploy Watsonx.data in a hybrid manner mixing cloud and on-prem storage, and they can bring multiple query engines to bear on the data stored there, Murali says. “The way we differentiate our lakehouse offering is that we are truly hybrid,” he tells Datanami. “You can deploy it anywhere–on-prem, cloud–but it’s also multi-engine.”

IBM’s new Watsonx includes three components: the Watsonx.data lakehouse, a collection of ML models in Watsonx.AI, and a data and AI governance solution called Watsonx.governance

Specifically, Watsonx.data users running in the IBM Cloud will use OpenShift Data Foundation (ODF) as the core object storage systems in COS, Murali says. However, users also have the option of running Watsonx.data on-prem if they want, in which case any S3-compatiable object store will work, including Minio or even the old Cleversafe object storage offering, which today is sold as part of COS. The underlying technology for managing these hybrid cloud storage setups is based on NooBaa, a data gateway acquired by Red Hat a few years ago, Murali says.

IBM is supporting PrestoDB as the core analytics engine for the Watsonx.data lakehouse. But it’s not the only engine that IBM will be pushing. When Watsonx.data goes GA next month, users will also see Apache Spark, which will enable users to bring more data engineering and data science-focused workloads into the lakehouse. IBM, of course, has a long history supporting Spark, so this is not a surprise.

But in addition to PrestoDB and Spark, IBM will bring Db2 and Netezza engines into the Watsonx.data lakehouse, Murali says. The plan is for those engines to be ready next month when the cloud lakehouse services become available, he says. Eventually, users will also be able to bring other open analytics engines, such as Dremio, to bear on the data, he says. (IBM did not deliver a clear answer when asked whether it would also support Trino, the fork of Presto backed by Starburst.)

One of the key pieces of technologies that allows so many open source engines to be used on the same Parquet, Avro, or ORC dataset without turning it into an ungoverned digital cesspool is Apache Iceberg. The open table format will help to keep all the data straight in Watsonx.data as multiple customers use multiple query engines to process it, Murali says.

“If they have Dremio or any other engine, they can choose to bring that,” he says. “We hope customers will come through Presto. But any engine they choose, we want them to come through that [Iceberg] metadata layer. That way we know what’s going on and we can maintain consistency across multiple engines.”

Much of the Presto ecosystem has rallied behind Iceberg, which came out of Netflix and Apple. But more is merrier in this new open world, and so there’s always room for another approach, which also applies to table formats. To that end, the company is actively working to ensure compatibility in Watsonx.data with Apache Hudi, which came out of Uber.

“I think this is why the Presto community shines,” says Girish Baliga, who is director of engineering at Uber and also the governing board chair of the Presto Foundation. “We have people who use it with different formats, and the engine allows us to do that pretty easily.”

There is a lot of momentum behind Iceberg in the Presto community, Baliga says, even though Hudi was already in development at Uber. “But from Uber’s perspective, more is better,” he tells Datanami. “I think putting up common layers that address all formats into the engine itself leads to a better, more open architecture.”

Embracing openness is certainly a strategy for IBM, which still has a large installed base of enterprise customers running Db2 and Netezza data warehouses, not to mention millions of tables of data (and plenty of old flat files) stored on proprietary Power and System Z mainframe systems. While there are no easy buttons when it comes to integrating this long tail of legacy IT systems with modern data stacks, IBM is clearly intent on doing all it can to lower the barrier of entry to get its customers to adopt newer tech, if not as a replacement mechanism then for new data projects.

“One of the main value-adds is how we package all of this together, where it’s easy and up and running probably in a few minutes, instead of the customer becoming the integrator,” Murali says. “Presto by itself is free. You can obtain it. You can install it. But what we want to help customers with is how easy it is to deploy, make administration of it easy, and fix vulnerabilities. That is something which is very, very key for our enterprise customers making sure that critical Sev One security vulnerabilities, all of those things are fixed and how we package the entire solution.”

You can register for PrestoCon Day here.

Related Items:

IBM Embraces Iceberg, Presto in New Watsonx Data Lakehouse

IBM Joins the Presto Foundation with Acquisition of Ahana

Open Table Formats Square Off in Lakehouse Data Smackdown

Mon, 05 Jun 2023 07:31:00 -0500 text/html https://www.datanami.com/2023/06/05/ibm-to-showcase-open-analytics-push-at-prestocon-day/
IBM Announces Record Breaking New Data Storage Device

On a Roll

Magnetic tape drives have been around for more than six decades now. It's commercial use has been mostly for storing data, such as tax documents and health care records, from mainframe computers. From the first 2-megabyte tape drives in the 1950s, today's versions are now capable of storing up to 15 terabytes. IBM has been pushing it further.

In partnership with Sony Storage Media Solutions, IBM has broken its previous record for the world's densest tape drive, announcing a product capable of storing 330 terabytes of uncompressed data. That's more storage than the world's biggest hard drives, capable of holding about 330 million books. The tape drive's cartridge could fit into the palm of a person's hand.

“The results of this collaboration have led to various improvements in the media technology, such as advanced roll-to-roll technology for long sputtered tape fabrication and better lubricant technology, which stabilizes the functionality of the magnetic tape," IBM fellow Evangelos Eleftheriou said in a statement, The Verge reported.

Advanced Storage

To achieve such storage capacity, IBM researchers had to develop new technologies, including advanced nanotech and new signal-processing algorithms. The end result was a tape that had an areal surface capable of storing 31 gigabits per cm² (201 gigabits per in²). Details of the device's development was published in the journal IEEE Transactions on Magnetics

Click to View Full Infographic

The end goal, of course, is commercial use. Specifically, IBM is looking to expand magnetic tape use to applications in the cloud. “Tape has traditionally been used for video archives, back-up files, replicas for disaster recovery, and retention of information on premise, but the industry is also expanding to off-premise applications in the cloud,” Eleftheriou said according to reporting from The Verge.

“While sputtered tape is expected to cost a little more to manufacture than current commercial tape, the potential for very high capacity will make the cost per terabyte very attractive, making this technology practical for cold storage in the cloud," he added.

Wed, 02 Aug 2017 06:46:00 -0500 text/html https://futurism.com/ibm-announces-record-breaking-new-data-storage-device
Syllabus and Course Development

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The Teaching and Learning Center (TLC) supports Drexel University instructors in course development, including the development of course learning goals and the design of assessments and learning activities to meet those goals. This site provides links to a number of resources that can assist instructors in that process, as well as links to important policies and information that instructors at Drexel should use in the creation of their syllabi. In addition to these resources, TLC consultants are available for individual consultations at any stage of the course and syllabus development process.

Drexel University Policies and Practices

Drexel University Student Services

Strategies and Best Practices

Fri, 27 Aug 2021 17:07:00 -0500 en text/html https://drexel.edu/teaching-and-learning/resources/syllabus-and-course-development/

eWeek has the latest technology news and analysis, buying guides, and product reviews for IT professionals and technology buyers. The site’s focus is on innovative solutions and covering in-depth technical content. eWeek stays on the cutting edge of technology news and IT trends through interviews and expert analysis. Gain insight from top innovators and thought leaders in the fields of IT, business, enterprise software, startups, and more.

Tue, 30 May 2023 06:12:00 -0500 en-US text/html https://www.eweek.com/cloud/
Top IBM Shareholders

International Business Machines Corp. (IBM) primarily generates revenue today through consulting, infrastructure, and software.

IBM was founded in 1911 as the Computing-Tabulating-Recording Company (C-T-R). But the computer giant traces its roots back to the 1880s. During that decade, Dr. Alexander Dey invented the first dial recorder for his business, while a second enterprise, Bundy Manufacturing, became the first time-recording company. Both companies became key building blocks of C-T-R.

More recently, IBM has become a global information technology company focused on software, cloud computing, and consulting services.

The top shareholders of IBM are Arvind Krishna, Michelle H. Browdy, James J. Kavanaugh, Vanguard Group Inc., BlackRock Inc., and State Street Corp. Below, take a closer look at them.

Key Takeaways

  • International Business Machines, better known as IBM, is one of the largest and most successful technology companies in the world.
  • IBM was a pioneer in computing, developing technology that led to personal computing.
  • The company is part of the Dow Jones Industrial Average and the S&P 500.
  • Its largest individual shareholders are Arvind Krishna, Michelle H. Browdy, and James J. Kavanaugh; all employees of IBM.
  • Its largest institutional shareholders are Vanguard Group Inc., BlackRock Inc., and State Street Corp

Top 3 Individual Insider Shareholders

The shareholders listed below have direct ownership. This list does not include indirect ownership of shares or shares accessible through stock options.

"Insider" refers to people in senior management positions and members of the board of directors, as well as people or entities that own more than 10% of the company's stock. In this context, "insider" has nothing to do with insider trading.

Arvind Krishna

Arvind Krishna owns 278,637 shares of IBM as of Feb. 21, 2023, representing 0.053% of all outstanding company shares. Krishna is CEO of IBM, after serving as Senior Vice President of Cloud and Cognitive Software, IBM’s fastest-growing business. He also headed IBM Research.

Krishna has been a key driver of IBM’s push into cloud computing in exact years, and as head of IBM Research, he guided the company through developments in blockchain, artificial intelligence, and quantum computing technologies. Krishna was a major architect of IBM’s acquisition of Red Hat.

Michelle H. Browdy

Michelle H. Browdy owns 113,542 shares of IBM as of Feb. 21, 2023, representing 0.02% of all outstanding shares. Browdy is Senior Vice President, Legal and Regulatory Affairs, and General Counsel. Prior to that, from 2012 to 2014, she served as Secretary to IBM's Board of Directors.

Before that role, she was IBM's worldwide head of litigation, where she was responsible for overseeing IBM's intellectual property and global litigation.

James J. Kavanaugh 

James J. Kavanaugh owns 90,955 shares of IBM as of Feb. 21, 2023, representing 0.017% of all outstanding shares. Since 2018, Kavanaugh has been Senior Vice President and Chief Financial Officer of IBM, overseeing the company’s global financial operations. In these roles, he also leads the company’s Global Financing business. Kavanaugh joined IBM in 1996 and has held a variety of financial leadership roles for the company, including Vice President of Finance for the Americas Group and IBM EMEA.

From 2008 to 2015, he was IBM’s Controller, and from 2015 to 2018, he was Senior Vice President, Transformation & Operations. In his current role, he continues to oversee Transformation & Operations, leading the company in aligning its operating model with fundamental industry shifts. Prior to IBM, Kavanaugh was the chief financial officer for the Americas Global Services unit at AT&T Corp.

Top 3 Institutional Shareholders

Institutional investors hold the majority of IBM shares at 56.93% of the total shares outstanding.

Vanguard Group Inc. 

As of March 31, 2023, Vanguard Group owns 81.2 million shares of IBM, representing 15.7% of total shares outstanding. The company is primarily a mutual fund and ETF management company. The Vanguard Information Technology ETF (VGT), which tracks a market-cap-weighted index of IT companies, owns IBM. The company represents about 1.34% of the fund's portfolio. This is not the only Vanguard fund that holds IBM.

BlackRock Inc.

As of March 31, 2023, BlackRock owns 71.6 million shares of IBM, representing 13.9% of total shares outstanding. The company is primarily a mutual fund and ETF management company. The iShares MSCI USA Value Factor ETF (VLUE), which invests in undervalued large- and mid-cap companies, owns IBM. IBM is the fourth-largest holding at 2.53% of the fund's portfolio. This is not the only BlackRock fund that holds IBM.

State Street Corp. 

As of March 31, 2023, State Street owns 53.5 million shares of IBM, representing 10.3% of total shares outstanding. State Street manages mutual funds, ETFs, and other investments. The SPDR Dow Jones Industrial Average ETF Trust (DIA), which tracks a price-weighted index of 30 large-cap U.S. stocks, holds IBM. IBM represents 2.51% of the fund's holdings. This is not the only State Street fund that holds IBM.

Is IBM Publicly Owned?

Yes, IBM is a publicly owned company and is on both the Dow Jones Industrial Average (DJIA) and the S&P 500.

What Is the Share Holding Pattern of IBM?

IBM is primarily held by institutional investors. In fact, of its 516,951,054 shares outstanding, 56.93% is held by 2,574 institutional holders.

Who Owns the Majority of Shares of IBM?

Vanguard Group is the largest shareholder of IBM, holding 15.7% of total shares outstanding as of March 31, 2023.

The Bottom Line

IBM is a large, multinational technology company that is also one of the oldest. The company pioneered personal computers and concentrates its business on hardware, software, and middleware. As one of the companies on the DJIA and the S&P 500, it is an important investment for many institutional investors.

Sun, 21 May 2023 12:00:00 -0500 en text/html https://www.investopedia.com/articles/insights/052216/top-5-ibm-shareholders-ibm.asp
IBM: Attractive Yield And Valuation
Milano, Italy. The IBM studios, the house of technological innovation in Milano. A building made of glass and wood. The building has the shape of a seed


Investment thesis

International Business Machines Corporation (NYSE:IBM) is a dividend champion with a fascinating history in general and over the past decade especially. The company experienced secular shifts and was forced to change to deal with declining financials. I consider that

Tue, 30 May 2023 20:55:00 -0500 en text/html https://seekingalpha.com/article/4608351-ibm-stock-attractive-yield-valuation
IBM doubles down on generative AI and hybrid cloud

Returning to live in-person events, our overriding impression from IBM Corp.’s annual Think event a couple weeks back was that the company showed unusual discipline by confining the focus to a couple of core themes: generative AI and hybrid cloud.

Given the hype around generative AI with the public preview of ChatGPT and the central role of Red Hat’s OpenShift as IBM’s platform modernization strategy, the choice of those themes was not surprising. What was surprising was that IBM stuck quite close to the script, as a stroll through the expo area reinforced.

From a product announcement standpoint, the spotlight was on the new watsonx family of products targeting, AI builders and data professionals. To the uninitiated, watsonx is not a typo; the branding is purposely all lower-case, which plays havoc with spellcheckers. The brand, which is distinguished from existing Watson, encompasses AI-based applications and tooling, focusing on AI model lifecycle management, AI model governance and a new data lakehouse.

Lower-case watsonx is supposed to represent a new generation of enterprise AI. The first generation was largely centered around machine learning, developing linear models using algorithms around regression, clustering, classification and so on. Not that machine learning, deep learning or neural networks are old hat or that IBM is moving away from them. Quite the contrary (and in fact, watsonx includes a number of “classical” capital-W Watson tools).

But here’s a reality check: Anecdotal conversations in passing with IBM Think attendees found most of their organizations still dipping their feet into machine learning, but likely taking for granted it that has already been embedded in the applications that they use day to day. And we’re also not omitting mention of deep learning or neural network models, but development of such complex models in the enterprise has to date only represented a tiny tip of the iceberg.

The foundation model train is leaving the station

IBM Research has actually been developing foundational models for the past three to four years but hasn’t exactly shouted about it until now. Neither had much of the rest of the industry, but all that changed with the hype around ChatGPT, which has dominated the news cycle this year. But consider this: Six months ago, how many people heard of GPT?

The new generative AI generation is premised on supply of foundation models that engorge sublime volumes of data with highly complex algorithms; such models would be practically impossible for mainstream enterprises to develop from scratch. The guiding notion is for organizations to start with a prebuilt foundation model and customize it for their needs. The good news is that IBM is showing that generative AI is not limited to Large Language Models, or LLMs, of the kind associated with GPT or Bard.

IBM is not alone here. Amazon Web Services Inc. recently announced private preview of its new Bedrock service that will include foundation models for LLM, text and conversation, and text-to-image that are run on specialized training and inference chips. For its part, Google LLC just unveiled a choice of foundation models for coding, image generation and conversation in addition to the rough-cut Bard LLM.

Initially, IBM is readying a series of models that are individually targeted for generative or traditional machine learning use cases. Like AWS and Google, IBM will address LLM, but also offer foundation models for geospatial, molecular chemistry (often used for drug discovery), information technology events (for addressing IT operations), code generation and documents (which could provide a form of knowledge management). IBM has identified “digital labor” (e.g., contact centers), IT automation, cybersecurity, sustainability and application modernization as the highest-demand use cases. For LLM models, we view code generation as the first likely killer app.

A key challenge for customers is navigating and choosing the right foundation model, or models, for the task. IBM will be prescriptive in some cases, designing a specific model for a specific use case. For instance, IBM is looking to enrich processes such as human capital management, procurement, and cybersecurity with specific models.

Because generative AI is still quite new, getting enterprises up to speed will initially require high-touch engagements, ranging from fixed-term jumpstarts to traditional consulting. In the long run, we’d like to see generative AI itself applied in helping organizations navigate through selecting the right foundational model, and providing guided experiences for customizing them. Yes, this could get quite meta.

Model lifecycle management

Watsonx will cover model build and lifecycle management environment. As such, it will draw upon classical upper-case Watson tools such as Watson Orchestrate, Watson Assistant and Watson Discovery, and introduce new ones (e.g., Watson Code Assistant), while replacing Watson Studio with a new environment for training and validation in the build stage, and tuning and model serving for production.

A highlight is the addition of a tuning workbench designed specifically for foundation models. Model governance, which is arguably part of the model lifecycle, will be handled concurrently through watsonx.governance.

The watsonx portfolio won’t be limited to IBM-supplied models but will also support the use of models harvested from the open-source wild such as Hugging Face. It will leverage open source enabling technologies and frameworks such as Ray, for scaling distributed compute, and PyTorch, for optimizing Python models for production.

The governance side will also be ecumenical in its reach across models. IBM adapted several capital-W Watson tools for AI governance along with capabilities from OpenPages, but with this proviso: These governance tools were designed for classical machine learning models. For generative AI foundation models, identifying practical approaches for governance is still a work in progress.

Models need data

The other piece of the puzzle is watsonx.data, which is IBM’s new data lakehouse based on Apache Iceberg. We’re not surprised as to IBM’s choice of Iceberg, as it is the open-source lakehouse table format that has garnered the most cross-industry support, and because IBM views Databricks Inc., which is behind Delta Lake, as a rival.

Although IBM is just the latest provider to support Iceberg, its implementation is differentiated with remote distributed caching, which allows organizations with data distributed across multiple physical instances to cache it where they want. And it supports hybrid cloud deployment. By contrast, most other lakehouse implementations restrict caching to the local cluster adjacent to where the data is physically stored.

IBM’s implementation also supports interoperability with Db2 Blu and Netezza, providing existing customers a lift-and-shift upgrade that allows them to take advantage of lakehouse capabilities, with the most important being the ability to extend ACID to data sitting in cloud object storage. This accomplishes two goals: By supporting bidirectional integration with Db2 Blu (Warehouse) and Netezza, IBM lives up to the requirement for hybrid cloud support. By integrating with the rudimentary Iceberg data catalog, IBM customers get access to popular open-source formats in the wild.

And, in IBM’s implementation, they can also use Spark and Presto open source query engines. We expect that IBM will subsequently update Watson Query (a.k.a., IBM Data Virtualization) to support these open source engines and, of course, connect to Iceberg.

But there’s another piece on our wish list. We would also like to see IBM make Python a first class citizen in the lakehouse, just as Snowflake Inc. has already done through its implementation for Iceberg.

Sorting it all out

With a new lower-case watsonx brand joining existing upper-case Watson, there’s bound to be confusion as to whether watsonx is the new, replacement version of Watson. The same goes for watsonx.data and Cloud Pak for Data; is one the replacement for the other?

In actuality, watsonx is the environment for building, running and governing AI models. But then again, there is IBM Cloud Pak for AIOps. Clearly, the existing Cloud Pak offering was geared around managing the lifecycle of machine learning, rather than more ambitious foundation models. Then there’s Cloud Pak for Data, which has been IBM’s primary data, analytics and AI environment for hybrid cloud and watsonx.data about the lakehouse.

Let’s zero in on governance. AI models feed on data and the algorithms, features and hyperparameters that comprise the model. The relevance of both model and data are closely intertwined. You could have technically correct data – that is, data that passes the right quality, currency, sovereignty/localization and security/access control requirements – but if the model is based on false assumptions, the house tumbles down.

And the reverse is true if the model is built with the right attributes: If the data is biased, or conditions change requiring different features, proverbially the surgery could still be successful, but the patient dies.

So, IBM not only needs to implement full data governance in watsonx.data, but it also needs to integrate the data and model governance functions so that neither functions are siloed or implemented as afterthoughts. Under watsonx, data and model governance are supposed to be concurrent activities. We believe that, at minimum, the activities should be coordinated and managed through a single pane of glass and, in the long run, have remediation capabilities such as sliding bar controls that could juggle model features, hyperparameters and data set selection.

By the way, IBM is hardly alone here. The data, AI and analytics industry still needs to figure this out.

The same goes with rationalizing Cloud Pak for Data with watsonx and IBM’s emerging intelligent data fabric architecture. As the lakehouse, with Apache Iceberg support, watsonx.data would be a logical extension of Cloud Pak for Data. It makes no sense for IBM to offer two separate “big data” technology stacks or product portfolios. And the data discovery, orchestration and governance capabilities that are delivered through the data fabric also play in.

With watsonx being a good start for delivering a coherent hybrid and multicloud build environment for AI models, we’d like to see IBM finish the job by integrating it with the Cloud Pak for Data portfolio. IBM states that these pieces fit together; our response is that they should not be separate pieces.

Tony Baer is principal at dbInsight LLC, which provides an independent view on the database and analytics technology ecosystem. Baer is an industry expert in extending data management practices, governance and advanced analytics to address the desire of enterprises to generate meaningful value from data-driven transformation. He wrote this article for SiliconANGLE.

Photo: IBM

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Tue, 23 May 2023 01:26:00 -0500 en-US text/html https://siliconangle.com/2023/05/23/ibm-doubles-generative-ai-hybrid-cloud/
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The AI studio, dubbed Watsonx.ai, will come with a foundation model library and necessary tools for data preparation, model development, and model monitoring, it added.

Copyright © 2023 IDG Communications, Inc.

Thu, 18 May 2023 16:49:00 -0500 en text/html https://www.infoworld.com/article/3695951/ibm-takes-on-aws-google-and-microsoft-with-watsonx.html

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