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C2090-603 exam Dumps Source : IBM System z Solution Sales V6

Test Code : C2090-603
Test designation : IBM System z Solution Sales V6
Vendor designation : IBM
: 52 existent Questions

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IBM IBM System z Solution

The AI Eye: IBM (NYSE: IBM) Watson fitness Lists 15 revise fitness programs examine and pleasant (NasdaqGS: quality) Actimize Enhances Surveillance solution

point Roberts, WA and Vancouver, BC - April 22, 2019 (Investorideas.com Newswire) Investorideas.com, a worldwide investor advice supply covering synthetic Intelligence (AI) brings you latest version of The AI Eye, gazing inventory information, deal tracker and advancements in artificial intelligence.

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The #AI Eye: IBM (NYSE: $IBM) Watson health Lists 15 excellent fitness programs study and excellent (NasdaqGS: $first-rate) Actimize Enhances Surveillance solution

Hear the AI Eye on Spotify

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IBM (NYSE:IBM) Watson fitness published its 15 proper health programs annual resolve today, leveraging superior facts, science and analytics to rank the correct-performing fitness programs in the U.S. in accordance with typical organizational performance. The 5 optimal-ranked tremendous health programs are Avera health in Sioux Falls, SD, Mayo basis in Rochester, MN, charity in Chesterfield, MO, St. Luke's fitness system in Boise, identity, and UCHealth in Aurora, CO. Ekta Punwani, one hundred top Hospitals application leader at IBM Watson health, noted:

"The multi-year trend toward increased healthcare consolidation has shined a spotlight on fitness device performance, illuminating each the challenges of coordinating freight across dissimilar facilities and the alternatives that can arrive with scale. The associations recognized in the IBM Watson fitness 15 arrogate fitness methods study are proposing a blueprint for a passage to extend first-rate, lessen fees and achieve surprising patient pride on a consistent foundation."

exceptional Actimize, a subsidiary of fine Ltd. (NasdaqGS:exceptional), has enhanced its smart eCommunications Surveillance solution with subsequent generation desktop researching capabilities, ingenious analytics and investigative tools to strengthen surveillance detection and case administration, reduce counterfeit signals, and power down compliance charges, based on a press release these days. Chris Wooten, government vice president, trait said:

"This latest release of their eCommunications surveillance answer, with its next technology laptop discovering and their award-successful ActOne investigation and case management solution, brings communications surveillance detection and investigative effectivity to a gross new degree."

Wearable AI Market to grow at 30% CAGR from 2019-2025

A document from global Market Insights finds that the Wearable AI market will develop at a compound annual growth expense (CAGR) of 30 % from 2019 to 2025, after surpassing $35 billion USD in 2018. An excerpt from the document summary reads:

The expanding disposable income of households in the rising economies is the primary component accelerating the adoption of smart wearables. over the past decade, the center classification of the emerging economies has increased greatly. in response to the world bank, the size of the center class within the Asia Pacific region improved from 525 million in 2009 to 1,700 million in 2018 and is further estimated to gain three,228 million via 2030. This boom is credited to the evolution of knowledgeable experts, speedy industrialization, and rising employment within the provider-based industries. due to this fact, a fundamental shift within the customer spending patterns toward buyer electronics product has been witnessed. this is encouraging smartwatch producers to capitalize on the altering subculture and upward propel in client spending patterns in emerging countries.

The rising focal point of consumers on fitness & fitness monitoring is riding the growth of the wearable AI market. As obesity and heart-connected illnesses are expanding throughout the globe, the demand for wearable gadgets among buyers to monitor their health ranges is additionally rising. health bands and smartwatches are equipped with a lot of sensors to computer screen numerous fitness-linked vitals, reminiscent of heartbeats and blood force, enabling clients to computer screen their fitness situations day by day without the intervention of any medical professionals. It moreover makes it workable for clients to monitor their fitness conditions in real-time in a price-beneficial manner, promotion the consume of sapient wearable instruments amongst patrons throughout the globe.

Sam Mowers, Investorideas.com

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IBM Expands device z options

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MongoDB proclaims aid for IBM z methods' Mainframe solutions

ny, ny and PALO ALTO, CA, Aug 17, 2015 (Marketwired via COMTEX) -- MongoDB today deepened its partnership with IBM, saying plans to present aid for MongoDB's products on IBM z techniques mainframe. This succor reflects the mighty momentum for mission essential deployments of the realm's quickest growing to breathe database with the aid of including assist for the unmatched velocity, security and availability of z methods.

The announcement comes as IBM launches LinuxONE, a new portfolio of hardware, utility and functions solutions based on the z systems mainframe that's designed for enterprise-grade Linux purposes.

"MongoDB has develop into the realm's quickest growing to breathe database by passage of enabling groups to without problems capitalize on the vitality of simultaneous applications and information to profit a aggressive skills," said Dev Ittycheria, president and CEO, MongoDB. "For years, the world's largest organizations possess flee necessary purposes on IBM mainframes. Their stream to champion IBM z systems is a testomony to their commitment to their users and shoppers to fabricate MongoDB available on outright essential structures. With this announcement, companies can now build and flee contemporary, mission-crucial purposes on confirmed mainframe technologies."

MongoDB is working carefully with IBM to engineer MongoDB commerce Server to breathe optimized for Linux on z programs and the new LinuxONE methods. As Part of the contract, MongoDB's international aid and engineering company will continue to collaborate with IBM to ensure company continuity for their joint purchasers operating MongoDB on IBM z techniques.

"we're consistently attempting to find gold standard-of-classification open supply technologies to set up on IBM z systems," talked about Ross Mauri, commonplace supervisor, IBM z systems. "Now, groups can amalgamate the imaginitive points of MongoDB with the unrivaled efficiency of the z programs mainframe to create solutions with new degrees of pace, scale and adaptability."

resources:

-- breathe a Part of the circulate and drill to develop into a MongoDB partner -- MongoDB is hiring! Browse open positions these days -- register for MongoDB college's free online classes -- breathe Part of MongoDB on LinkedIn and result @MongoDB on Twitter

About MongoDB MongoDB is the subsequent-generation database that helps businesses seriously change their industries by passage of harnessing the energy of information. the realm's most sophisticated groups, from chopping-side startups to the biggest businesses, consume MongoDB to create applications by no means earlier than feasible at a fraction of the cost of legacy databases. MongoDB is the fastest-turning out to breathe database ecosystem, with over 10 million downloads, thousands of valued clientele, and over 1,000 expertise and service companions. learn more at www.mongodb.com.

Press Contacts Jennifer Seelin MongoDB, North america (631) 827-2272 Jennifer.Seelin@MongoDB.com Jack Costley MongoDB, international +44 75 00 56 16 ninety five Jack.Costley@MongoDB.com Joe man Collier IBM Media family members (248) 990-4707 jgcollie@us.ibm.com

source: MongoDB, Inc.

(C) 2015 Marketwire L.P. outright rights reserved.


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IBM System z Solution Sales V6

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Factors Expected to Influence IBM This Earnings Season

International commerce Machines IBM is set to report first-quarter fiscal 2019 results on Apr 16. Notably, the company surpassed the Zacks Consensus evaluate in the trailing four quarters with middling beat of 1.39%.

In the last reported quarter, the company delivered non-GAAP earnings of $4.87 per share, beating the Zacks Consensus evaluate of $4.81 per share. However, earnings per share (EPS) decreased 5.9% from the year-ago quarter. The year-over-year decline in EPS can breathe attributed to higher tax rate.

Revenues of $21.76 billion were almost in line with the Zacks Consensus evaluate of $21.74 billion. However, the pattern declined 3.5% on a year-over-year basis. At constant currency (cc), revenues dipped 1%. The year-over-year decline can primarily breathe attributed to currency fluctuation and headwinds from IBM Z product cycle.

Guidance & Estimates

The Zacks Consensus evaluate for the to-be-reported quarter is pegged at $18.65 billion, down roughly 2.2% from the year-ago quarter. Further, the consensus trace for earnings is pegged at $2.22 per share, indicating year-over-year decline of 9.4%.

IBM expects non-GAAP EPS forecast for 2019 to breathe at least $13.90. The Zacks Consensus evaluate for earnings is pegged at $13.91 per share, up marginally 7% year over year.

Let’s perceive how things are shaping up prior to this announcement.

International commerce Machines Corporation cost and EPS Surprise

 

International commerce Machines Corporation cost and EPS amaze | International commerce Machines Corporation Quote

Factors to Consider

IBM’s initiatives in blockchain, cloud and enterprise AI market through product rollouts and strategic deals bode well. Moreover, the company’s improving clout in the cloud, security and analytics remains a tailwind.

In the fourth quarter, Strategic Imperatives (cloud, analytics, mobility and security) grew 5% from the year-ago quarter to $11.5 billion.

Cloud revenues surged 6% from the year-ago quarter to $5.7 billion and 19% (excluding IBM Z product cycle impact).

Coming to Systems, the segment comprises both hardware and operating systems software revenues. Broad-based adoption of the z14 mainframe, power systems and sturdy shimmer sales remain key catalysts for the segment.

Systems revenues decreased 21% on a year-over-year basis (down 20% at cc) to $2.6 billion, primarily owing to impact of the IBM Z product cycle. Segmental revenues pertaining to Strategic Imperatives plunged 22%, while Cloud revenues declined 31%.

IBM Z revenues decreased 44% year over year. However, MIPS capacity has increased around 20%, driven by broad-based adoption of the z14 mainframe.

Cognitive Solutions’ revenues-external increased 2% year over year (on cc basis) to $5.5 billion. Revenues from Cognitive Solutions (including solutions software and transaction processing) increased primarily on account of growth in solutions software, including analytics and artificial intelligence (AI). The Zacks Consensus evaluate for the first quarter is currently pegged at $4.14 billion.

Revenues from Global commerce Services-external segment were $4.3 billion, up 4% from the year-ago quarter (up 6% at cc). The year-over-year extend was primarily due to growth across outright three commerce areas namely consulting, application management and global process services. The Zacks Consensus evaluate for the first quarter is pegged at $4.23 billion.

Revenues from Technology Services & Cloud Platforms-external decreased 3% from the year-ago quarter (flat at cc) to $8.9 billion. The Zacks Consensus evaluate for the first quarter is pegged at $8.27 billion.

Product Rollouts & Strategic Deals: Key Catalysts

IBM recently completed the launch of its next generation POWER9 processors for mid-range and high-end systems. These are designed for handling advanced analytics, cloud environments and data-intensive workloads in AI, HANA, and UNIX markets.

The company moreover introduced new offerings optimizing both hardware and software for AI. Management believes that products relish PowerAI Vision and PowerAI Enterprise will succor drive new customer adoption.

Story continues

IBM expanded partnership with Vodafone Group. Per the deal, IBM’s advanced hybrid cloud platform, AI, and IoT capabilities will aid Vodafone commerce with digital transformation initiatives.

IBM moreover announced that IBM Cloud will breathe utilized by RemoteMyApp, a game streaming startup based out of Poland. IBM Cloud will aid RemoteMyApp to expand global market gain and enhance its flagship gaming platform, Vortex.

The robust growth in cloud gaming market and increasing focus on video game streaming platforms presents significant growth chance for IBM.

Further, IBM cloud is witnessing increasing adoption across businesses based in Europe as evident from this deal. Notably, IBM Cloud offers local EU-based champion to its clientele by which access can breathe confined to solely EU-based IBM employees. These factors are aiding the tech giant to expand foothold in Europe

IBM is moreover benefiting from notable customer wins, especially in the sports domain of late. IBM is introducing several initiatives to bolster experience of the entities involved in sports leagues, from players, coaches to fans and management.

The company’s bid to integrate AI into sports systems’ is in sync with its strategy to depart beyond fan engagement to influencing players with robust strategies. These innovative solutions aided by IBM’s Watson positions the company well in gaining momentum.

IBM is moreover gaining momentum in blockchain technology. The traction witnessed by the company’s Watson Ads offering is bolstering the top line, in turn aiding IBM to better compete against peers.

While these developments are expected to positively impact results, IBM’s continuing investments and pricing pressure related to its legacy hardware commerce and ballooning debt levels remain headwinds.

What the Zacks Model Unveils

According to the Zacks model, a company with a Zacks Rank #1 (Strong Buy), 2 (Buy) or 3 (Hold) has a proper haphazard of beating estimates if it moreover has a positive Earnings ESP. The Sell-rated stocks (Zacks Rank #4 or 5) are best avoided.

IBMhas a Zacks Rank #3 and an Earnings ESP of 0.00%. You can uncover the best stocks to buy or sell before they’re reported with our Earnings ESP Filter.

Stocks with auspicious Combination

Here are a few stocks that are worth considering as their model shows that these possess the birthright combination of elements to deliver an earnings beat in the upcoming releases.

The Progressive Corporation PGR has an Earnings ESP of +0.09% and a Zacks Rank #2.You can see the complete list of today’s Zacks #1 Rank stocks here.

RLI Corp. RLI has an Earnings ESP of +2.56% and a Zacks Rank #2.

Netflix, Inc. NFLX has an Earnings ESP of +0.44% and a Zacks Rank #3.

Radical New Technology Creates $12.3 Trillion Opportunity

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Large businesses don’t possess to breathe lousy innovators

Creative Construction: The DNA of Sustained Innovation

by Gary P. Pisano, PublicAffairs, 2019

Gary Pisano doesn’t buy the credence that great enterprises are inherently lousy innovators. Back in 2006, Pisano, the Harry E. Figgie Professor of commerce Administration at Harvard commerce School, traced the inception of every drug approved by the FDA over a 20-year age to either one of the world’s 20 largest pharmaceutical companies or one of the 250 smaller, supposedly more innovative biotechs. When he compared the two groups, he discovered a “statistical deceased heat” — R&D productivity was no better in the smaller biotechs than in Big pharma.

Pisano moreover points to anecdotal evidence to champion his opposition to the conventional wisdom about innovation in great enterprises. For every big, established company that failed at transformational innovation (think Blockbuster, Kodak, and Polaroid), he points to another that has succeeded. In 1964, when IBM announced its revolutionary 360 mainframe computers, it was already the largest computer company in the world and ranked 18th on the Fortune 500. In 1982, when Monsanto scientists invented the foundational technology for GMOs (genetically modified organisms), the company was 81 years passe and number 50 on the Fortune 500. And in 2007, when Apple launched the iPhone, it had sales of US$24 billion and already stood at 123rd on the Fortune 500.

Pisano says that the contrast between a Blockbuster and an IBM is the competence of leaders to sustain and rejuvenate the innovation capacity of their companies. It’s an competence he calls “creative construction,” and he writes that it “requires a delicate equilibrium of exploiting existing resources and capabilities without becoming imprisoned by them.”

Walking that tightrope is a challenge for great companies. It’s tough to hurry the needle with innovation when the needle’s scale is measured in billions of dollars. “For J&J [Johnson & Johnson] to maintain its historical rate of top-line growth,” reports Pisano, “it must generate about $3 billion–$4 billion of new revenue per year.” The complexity of managing innovation in great organizations can moreover breathe daunting. “When you secure to breathe the scale of a J&J, you possess a lot of lamentable parts,” he explains. “You now possess a system with sober frictions. Friction impedes mobility. want of mobility means want of innovation.”

But great companies moreover possess some advantages that can give them a leg up in innovation. “Larger enterprises relish J&J possess massive monetary resources to explore new opportunities,” says Pisano. They can hedge their bets, tap abysmal reservoirs of talent, navigate regulatory agencies, and consume their huge distribution networks and sturdy brands to roll out new products to millions of existing customers.

So how finish leaders of great companies meet the challenges and leverage the benefits of innovation? Pisano’s retort to that question is the core content of Creative Construction, a readable practicum of a book aimed at senior leaders. He writes, “Building an organization’s capacity to innovate involves three essential leadership tasks: (1) creating an innovation strategy, (2) designing an innovation system, and (3) edifice an innovation culture.”

Bromides, such as “eat your own lunch before someone else does,” are the bane of innovation strategy, according to Pisano. bewitch IBM’s mainframe business: “Eat your own lunch” suggests that IBM should possess cannibalized its success and jumped wholeheartedly into the PC commerce back in the 1980s. But it didn’t, and now that cloud computing and Big data are ubiquitous, mainframes are back and IBM’s System Z dominates the market. “In 2017, almost 90 percent of outright credit card transactions were processed by an IBM mainframe,” reports Pisano.

Bromides, such as “eat your own lunch before someone else does,” are the bane of innovation strategy.

Instead of chasing clichés, says the author, leaders necessity to map and clarify their options for both product and business-model innovation. Then, they must arrogate their resources according to the best available choices in terms of value creation and capture.

The successful execution of an innovation strategy requires what Pisano calls an innovation system. He writes, “Innovation systems necessity to fulfill three basic tasks: (1) search for novel and valuable problems and solutions, (2) synthesis of diverse streams of ideas into coherent commerce concepts, and (3) selection among opportunities.”

The last of these tasks — selection — is where many of the exemplars of innovation failure possess tripped up. “Xerox, AT&T, and Polaroid didn’t fail from want of competence to learn a problem (search) or bring together diverse ideas into a novel solution (synthesis),” argues Pisano. “They failed from an inability to fabricate proper project-selection decisions.”

The creation of an innovation-friendly culture is, of course, equal in import to strategy and systems. After all, writes Pisano, “innovation is a deeply human activity.” He lists the habitual norms of innovation-friendly cultures, including “a tolerance for failure, license to experiment, willingness to talk up, openness to collaboration, and less hierarchy.” But he tempers each norm, explaining, “Innovative cultures possess a rougher side as well. These less palatable practices, though, are critical complements to the more pleasant ones.” So, for example, an efficacious innovation culture should possess a “tolerance for failure but no tolerance for incompetence” and a “willingness to experiment but [also be] highly disciplined.”

Pisano’s three-part strategy for creative construction isn’t earthshaking in its particulars — strategy, systems, and culture are the broadly defined components of any organizational capability. But then, I’d squabble that nothing of the capabilities upon which the long-term success of great enterprises depends are particularly earthshaking. Instead, they must breathe comprehensively imagined and rigorously pursued. In Creative Construction, Pisano does an uncommonly proper job of filling the former Part of that bill; the latter Part is up to senior leaders.


IBM Improves IT Operations with artificial Intelligence

Artificial Intelligence in IT Today

Many IT departments possess implemented software solutions that depart beyond simple transaction and analytical processing. These packages hold models that picture inevitable data behaviors, and these models consume current data to perceive if these patterns of data behavior exist. If so, operational systems can consume this information to fabricate decisions. A proper instance of this is fraud detection. IT data engineers consume analytics on historical data to determine when fraud occurred, code this into a model, and deploy the model as a service. Then, any operational system can invoke the model, pass it current data and receive a model “score” that represents the probability that a transaction may breathe fraudulent.

The universal term for these new packages is artificial intelligence (AI). They consist of a combination of search, optimization and analytics algorithms, statistical analysis techniques and template processes for ingesting data, executing these techniques and making the results available as services called models. The subset of AI that deals with model creation and implementation is sometimes called machine learning (ML).

Machine Learning and artificial Intelligence

IT departments implement ML and AI solutions in the broader context of their data and processing footprint. This is usually depicted as the following four-layer hierarchy.

Layer 1: The Data.

This layer contains the data distributed across the enterprise. It includes mainframe and distributed data such as product and sales databases, transactional data and analytical data in the data warehouse and any Big data applications. It moreover may embrace customer, vendor and supplier data, perhaps at remote sites, and even extends to public data such as twitter, advice feeds and survey results. Another workable source of data is server performance logs that embrace resource usage history.

Note that these data exist across diverse hardware platforms including on-premises and cloud-based. As such, various data elements can exist in multiple forms and formats (e.g. text, ASCII, EBCDIC, UTF-8, XML, images, audio clips, etc.). In addition, at this flush will exist hardware and software that manage the data, including high-speed data loaders, data purge and archive processes, publish-and-subscribe processes for data replication, as well as those for standard backup and recovery and cataclysm recovery planning.

Layer 2: The Analytics Engines.

In this layer exist a compund of hardware and software that executes commerce analytics against the data layer. There are several common players in this space. They include:

  • The IBM Db2 Analytics Accelerator (IDAA) than can breathe implemented as standalone hardware or fully integrated within inevitable z14 servers;
  • Spark on z/OS;
  • Spark Anaconda on z/OS;
  • Spark clusters on distributed platforms.
  • Just as the data layer occurs across multiple hardware platforms and distributed sites, so will the analytics engines layer. The major role of this layer is to provide an optimized data access layer against the underlying data as a service for AI and operational applications.

    Layer 3: The Machine Learning Platform.

    IT implements machine learning software in this layer. It accesses the data through one or more of the analytics engines. It is in this layer that IBM delivers its latest offering, Watson Machine Learning for z/OS (WMLz). WMLz provides a basic machine learning workflow consisting of the following steps:

  • Data Ingestion and Preparation — Inputting data, filling in missing values, encoding category data, creating indexes and normalizing numeric values;
  • Model edifice and Training — An interface for the data scientist to create a model of data behavior based on historical analytics, train the model to detect data patterns and validate the model;
  • Model Deployment — Implement the model as a production process, including procedures for updating models in-place and monitoring model results;
  • Feedback Loops — Processes that allow automated model learning by feeding model results back into the model training process to update models or succumb new ones.
  • Data scientists know that one of the greatest benefits of machine learning is to consume the results in operational systems; for example, having an ML model resolve monetary data to determine the possibility of fraud. This means that you will achieve best performance when you deploy ML in the hardware environment where transaction processing occurs. For many great organizations this means the IBM zServer environment.

    Layer 4: Machine Learning Solutions.

    Now that they possess the machine learning platform available as ML services, they can create combined AI/ML solutions that invoke those services. IBM has several ready-made solutions for this layer, including the following:

  • Db2 AI for z/OS (Db2ZAI) -- Using Db2 SMF data for analysis, Db2ZAI monitors and analyzes Db2 operations in a Z/OS environment. It can provide improved query access path information to the Db2 optimizer to extend SQL performance, diagnose Db2 performance abnormalities and recommend corrective action and detect Db2 statistics anomalies and provide performance tuning recommendations;
  • IBM Z Operations Analytics (IZOA) -- This product analyzes z/OS SMF data and detects changes in subsystem consume and forecasts changes that may breathe required in the future, does automatic problem analysis and provides problem insights from known problem signatures.
  • Watson Machine Learning on Z

    Let’s bewitch a deeper dive into how Watson Machine Learning on Z (WMLz) works and what services it can provide.

    Key Performance Indicators (KPIs). WMLz does not inherently know what performance factors are necessary to you. However, once these KPIs are defined (either by a user or by implementing one of the machine learning solutions celebrated above), WMLz can resolve KPI data to contemplate for correlations. For example, when one KPI (say, I/O against a critical database) goes up, another KPI (say CPU usage) may depart up as well. As another example, several KPIs may breathe behaviorally similar, so WMLz can cluster them as a group and fulfill further analysis across groups. WMLz can moreover determine KPI baseline behaviors based on time-of-day, time zone of transactions or seasonal activity.

    Anomaly Detection. Once correlations are discovered, WMLz can contemplate antithetical effects and report them as anomalies. In their I/O instance above, an anomaly would breathe reported if I/O against a critical database increased but CPU usage decreased.

    Pattern Recognition. As with many machine learning engines, WMLz will contemplate for patterns among KPIs and data identifiers. For example, CPU may extend when processing inevitable categories of transactions.

    KPI prediction. An extension of basic KPI processing, WMLz can consume the past behaviors of groups of KPIs to forecast the future. reckon their I/O instance once again. The product may detect that inevitable transactions become more numerous during a particular time period, and these transactions consume significantly more CPU cycles. The product may then forecast future CPU spikes.

    Batch workload analysis. Many IT shops possess a great contingent of batch processing that is tightly scheduled and includes job and resource dependencies. Some jobs must wait for their predecessors to complete, some consume significant shared resources (such as tape drives or specialty hardware) and some are so resource-intensive that then cannot breathe executed at the selfsame time. WMLz can resolve the workload data, including resource usage, and provide recommendations for balancing resources or tuning elapsed times.

    MLC cost pattern analysis and cost reduction. Some IBM software license charges are billed monthly, and the license amount may depend upon maximum CPU usage during peak periods. WMLz can resolve CPU usage across time, contemplate for patterns and fabricate predictions and recommendations for software license cost reduction.

    Watson Machine Learning for z/OS — Features

    IBM’s Watson Machine Learning for z/OS allows IT its selection of evolution environments to develop models including IBM SPSS Modeler. These environments assist data scientists by using notebooks, data visualization tools and wizards to accelerate the evolution process. Several quick-start application templates are moreover incorporated in the toolset for common commerce requirements such as fraud detection, load approval and IT operational analytics. The latest version of WMLz (version 2.1.0) includes champion for Ubuntu Linux on Z, java APIs, simplified Python package management and several other features.

    Interested readers should reference the links below for more detailed technical information.

    # # #

    See outright articles by Lockwood Lyon

    REFERENCES

    Machine Learning and artificial Intelligencehttps://en.wikipedia.org/wiki/Machine_learning

    Data and AI on IBM Zhttps://www.ibm.com/analytics/z-analytics

    Using Anaconda with Spark — Anaconda 2.0 documentationhttps://docs.anaconda.com/anaconda-scale/spark/

    Watson Machine Learning - Overviewhttps://www.ibm.com/cloud/machine-learning

    Watson Machine Learning - Resourceshttps://www.ibm.com/cloud/machine-learning/resources



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