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IBM
C1000-024
IBM Grid Scale Cloud Storage V2
http://killexams.com/pass4sure/exam-detail/C1000-024 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 customer’s 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
switches?
A . iSCSI
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
snapshots?
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
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https://killexams.com/exam_list/IBMCourse 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 scheduleÂ
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 competenciesÂ
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
--Â 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.)Â
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]Â
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 -0600entext/htmlhttps://www.slu.edu/provost/faculty-affairs/teaching-resources-for-faculty/course-syllabus-information/index.phpIBM to Showcase Open Analytics Push at PrestoCon Day
(nialowwa/shutterstock)
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.
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.”
Mon, 05 Jun 2023 07:31:00 -0500text/htmlhttps://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 Vergereported.
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.Â
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 -0500text/htmlhttps://futurism.com/ibm-announces-record-breaking-new-data-storage-deviceSyllabus and Course Development
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Fri, 27 Aug 2021 17:07:00 -0500entext/htmlhttps://drexel.edu/teaching-and-learning/resources/syllabus-and-course-development/Cloud
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Tue, 30 May 2023 06:12:00 -0500en-UStext/htmlhttps://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. KavanaughÂ
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.Â
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.Â
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 -0500entext/htmlhttps://www.investopedia.com/articles/insights/052216/top-5-ibm-shareholders-ibm.aspIBM: Attractive Yield And Valuation
Mrkit99
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 the company succeeded in facing massive restructuring, and its cash flow metrics are still stellar. The dividend yield together with valuation looks attractive to me.
Company information
IBM is a leading U.S.-based enterprise IT hardware, software, and services provider. The company has a vibrant history tracing back to 1911.
The company's fiscal year ends on December 31. The company has been restructuring its business over the past several years to address changing technological environment. To align with the new structure, the company has revised its segment reporting into four major categories: Software, Consulting Infrastructure, and Financing.
Compiled by the author based on the latest IBM 10-K
Financials
If we look at the company's financials over the past decade, we can see that the company has been changing its business significantly to survive in the changing technological environment.
Author's calculations
The company's revenues were declining substantially between FY 2013 and FY 2020 due to a secular decline in traditional business which significantly decreased demand for mainframes and servers. The industry faced technological disruptions that changed the overall business landscape of IBM. Increased adoption of cloud computing, mobile devices, and data analytics made legacy hardware look like a dinosaur. IBM had to adapt to the changing market dynamics and transform its products and services to be cloud-compatible. This transition involved significant investments in cloud infrastructure and services, impacting the company's financial performance in the short term.
As we can see from the financial dynamics over the past three years, we can see that the company succeeded in the transformation. I consider it to be successful because of the expanded gross margin over the decade and the operating margin turning back to double-digits from the 8.5% bottom in FY 2020.
What I also like about IBM's financial performance is its exceptional free cash flow [FCF] margins which the company managed to sustain regardless of the very harsh secular headwinds it faced. For me, it is a solid quality sign of the management, which allowed the company to demonstrate an A+ dividend consistency over the past quarter century.
Seeking Alpha
Apart from strong dividend consistency, Seeking Alpha Quant assigns IBM stock the highest possible dividend yield grade of "A+", and I agree with it because the company's current forward dividend yield is above 5% which I consider attractive, especially in the current environment where inflation dipped below 5% and is on the path to its historical averages.
Seeking Alpha
I consider IBM's financial position as strong as well. Liquidity looks sound and the outstanding cash of $17 billion also indicates that the balance sheet is in good shape. Critics might argue that the total debt-to-equity ratio of about 300% is a red flag, but I do not think so since the company's debt has been historically much higher than both equity and cash due to the capital allocation strategy. So, I do not see high risks in substantial debt amounts. The major part of the debt is long-term, so I see low risks here.
Seeking Alpha
The company reported its Q1 FY 2023 earnings on April 19, slightly missing on the topline but beating consensus estimates for the EPS. GAAP revenue was flat on a YoY basis. In constant currency and adjusted for M&A, revenue rose 4% year-over-year. IBM's business is highly seasonal so I do not compare QoQ here. The non-GAAP gross margin was 53.7% in Q1 FY 2023 versus 52.9% a year earlier.
I would like to underline two factors I like in the current challenging and uncertain environment. First, IBM has minimal exposure or manufacturing presence in China which is good given rising geopolitical tensions between the world's two biggest economies. Second, IBM is focused on the enterprise market, while many other technology companies rely on consumer business and are currently vulnerable to the softening demand for personal electronics devices.
Overall, I believe that IBM's financial performance is strong, and the company is well-positioned to weather potential storm.
Valuation
As seen in the "Financials" section, IBM has consistently paid out dividends to its shareholders. Therefore, I have one more option other than discounted cash flows [DCF] for valuation. Let me start with the dividend discount model [DDM]. To start with underlying assumptions, let me determine the discount rate first. I consider 8.4% WACC which is provided by valueinvesting.io to be reasonable. I also have dividend consensus estimates projected at $7.04 per share in FY 2024. For dividend growth, I take the average between the historical 5-year average forward dividend growth rate and 3Y CAGR, which is 4% if rounded.
Seeking Alpha
Incorporating all the above assumptions into the DDM formula gives me a fair share price of $160, 24% higher than the current stock price. Looks attractive to me but let me cross-check with the DCF approach.
Author's calculation
The discount rate would be the same as I used for the DDM approach. For future revenues, I use consensus earnings estimates, the revenue is expected to grow at about 3% CAGR which I consider modest. For the FCF margin, I prefer to be conservative and use the average for the last decade, which was at 13.5% with SBC deducted.
Author's calculations
As you can see from the above spreadsheet, the company's fair business value is substantially higher than the current market cap. Actually, it is about 2 times higher than the market cap, indicating massive undervaluation.
To conclude, I think that IBM is very attractively valued at current levels. If we add up about 5% dividend yield, I believe that the stock can be a compelling investment opportunity, but before we make a final decision let me discuss significant risks in the next section.
Risks to consider
As a technology company, IBM faces fierce competition and high technological disruption risks. IBM must actively monitor industry trends, invest in cutting-edge technologies, and foster a culture of innovation to navigate these challenges successfully. On the other hand, based on the history of overcoming secularly unfavorable shifts for IBM over the past decade, we can conclude that the management is strong in innovating and reorganizing.
As I mentioned in the "Financial" section, IBM's business is well-positioned in the current challenging environment. But the recession and credit crunch are still looming, and it will highly likely affect all stock prices to go down if the worst scenarios unfold. Therefore, there is a risk of an overall stock market downturn, and IBM is highly likely to follow the same direction.
Bottom line
Overall, I believe that IBM stock looks like an attractive investment opportunity. Despite the high risks of the potential overall market downturn, I consider IBM as a solid long-term bet given its attractive valuation and high dividend yield. I assign IBM stock a buy rating because I believe that the benefits outweigh the risks here.
Tue, 30 May 2023 20:55:00 -0500entext/htmlhttps://seekingalpha.com/article/4608351-ibm-stock-attractive-yield-valuationIBM 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.
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.
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Tue, 23 May 2023 01:26:00 -0500en-UStext/htmlhttps://siliconangle.com/2023/05/23/ibm-doubles-generative-ai-hybrid-cloud/IBM News
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Sat, 15 Aug 2020 13:04:00 -0500entext/htmlhttps://cointelegraph.com/tags/ibmIBM takes on AWS, Google, and Microsoft with Watsonx
IBM is taking on the likes of Microsoft, AWS, and Google by introducing Watsonx, a new generative AI platform, which will help enterprises design and tune large language models (LLMs) for their operational and business requirements.
Watsonx comes with a suite of tools for tuning LLMs, a data store built on lakehouse architecture, and an AI governance toolkit, the company said.
Watson AI is IBM’s artificial intelligence engine that the company had trained on different machine learning algorithms along with question analysis, natural language processing, feature engineering, and ontology analysis. Watsonx can be seen as the evolution of Watson AI.
With the Watsonx platform, the company said it is trying to meet enterprises’ requirements in five areas, including interacting and conversing with customers and employees, automating business workflows and internal processes, automating IT processes, protecting against threats, and tackling sustainability goals.
IBM Watsonx.ai to help fine-tune large language models
As part of the generative AI platform, IBM will offer a development studio for AI builders to train, test, tune, and deploy traditional machine learning and new generative AI capabilities with the help of built-in foundation models, the company said.
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.
Examples of some components of Watsonx.ai include a Tuning Studio, a Prompt Lab, the foundational model library, developer libraries, and APIs.
“Foundation models make deploying AI significantly more scalable, affordable, and efficient. With IBM Watsonx, clients can quickly train and deploy custom AI capabilities across their entire business, all while retaining full control of their data,” IBM CEO Arvind Krishna said in a statement.
The current foundation models included in the library have been trained to understand not only natural language but also code, time-series data, tabular data, geospatial data, and IT event data, IBM said, adding that the initial set of foundation models will be made available in beta tech preview to select clients.
Some of the foundation models in Watsonx.ai’s library are fm.code, fm.NLP, and fm.geospatial.
While fm.code can be used to train models to generate code for developers via natural language processing, fm.geospatial can be used to predict weather or climate conditions as it is a model built on climate and remote sensing data.
The fm.geospatial model was built by IBM Research in collaboration with NASA, the company said. The AI studio’s components such as the Prompt Lab currently only support tuning of text and code foundation models.
Under fm.NLP, the company is offering a collection of LLMs that can be customized using client data for better natural language understanding as language or expressions can vary from one industry to the other.
IBM has also partnered with Hugging Face to provide datasets and models built on Hugging Face’s open source libraries within Watsonx.ai.
Watsonx.ai, which will be available as a SaaS offering initially, is expected to be made available in July this year, the company said.
IBM Watsonx.data to act as a data store
Along with Watsonx.ai, IBM is introducing a data store, which is built on an open lakehouse architecture, for AI workloads.
The data store, dubbed Watsonx.data, will support open data formats and help enterprises with additional capabilities such as data querying and data governance among others.
It can reduce data warehousing costs by 50%, IBM said, adding that the data store offers integrations with an enterprise’s existing databases.
Watsonx.data, which is expected to be generally available in July 2023, supports both on-premises and multicloud environments, the company said.
Big Blue is also adding an AI governance toolkit inside Watsonx, dubbed Watsonx.governance, to enable enterprises to build trusted workflows.
Watsonx.governance, which is expected to be generally available later this year, operationalizes governance to help mitigate the risk, time, and cost associated with manual processes, the company said, adding that it provides the documentation necessary to drive transparent and explainable outcomes.
Watsonx vs Google, AWS, and Microsoft
IBM’s move to add generative AI-building capabilities comes at a time when rival vendors such as Microsoft, AWS, and Google have already announced similar services.
In March, Microsoft released its Azure OpenAI APIs that offered prompt engineering. The following month, AWS released a new service, dubbed Amazon Bedrock, that provides multiple foundation models designed to allow companies to customize and create their own generative AI applications — including programs for general commercial use.
IBM’s efforts to cash in on the generative AI wave with Watsonx might not move the needle too much, according to Andy Thurai, principal analyst at Constellation Research.
“Among the Watsonx announcements, AI studio and data store won't move the needle much. They are catching up, if that, with other vendors who have been offering better AI data stores and data lakes for many years,” Thurai said.
However, Thurai seemed optimistic about the AI governance toolkit that IBM is offering as part of the platform.
“The toolkit allows companies to build trusted AI workflows and help with building explainable and transparent AI workflows. Though it is not available currently, slated to be released late this year, this can be differentiation in the crowded AI market,” the principal analyst said.
Watson Code Assistant
IBM is also planning to infuse Watsonx.ai foundation models throughout its major software products going forward, including the Watson Code Assistant.
Watson Code Assistant, which is expected to be made available later this year, will allow AI developers to generate code via natural language processing.
“Currently, Watson Code Assistant is focused on increasing developer productivity for IT automation with Red Hat Ansible. We anticipate expanding to other domains in the future,” an IBM spokesperson said.
“Other features such as content discovery, code optimization, and explanation of code are all part of IBM’s vision for Watson Code Assistant as the product expands its capabilities,” the spokesperson added.
In contrast, rival products, such as Amazon CodeWhisperer, Google’s Bard AI, and Microsoft-owned GitHub Copilot, already offer such capabilities and are trained on languages other than Python.
In addition to the Code Assistant, IBM will also offer an AIOps Insights tool for visibility into IT operations.
Watsonx.ai will also be integrated into IBM’s digital labor products, the company said, adding that a new generative AI-powered suite for managing environmental goals will be offered separately later this year.
IBM’s consulting unit, IBM Consulting, has announced a Center of Excellence for generative AI, staffed with over 1,000 generative AI experts.
With the help of the new center, IBM plans to build a Watsonx-focused practice, which will actively build and deploy Watsonx for clients, the company said.
The new practice around Watsonx can help IBM in generating revenue, according to Thurai.
“This practice, with over 1000 experts, who are focused on WatsonX practices, can be developer advocates in creating traction for the technology with the customers. This is one of the areas where most of the other AI companies struggle — to create an army of practitioners to help customers,” Thurai said.
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