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000-600 System z Solution Sales V4

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000-600 exam Dumps Source : System z Solution Sales V4

Test Code : 000-600
Test Name : System z Solution Sales V4
Vendor Name : IBM
: 57 Real Questions

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IBM System z Solution Sales

IBM Q1 shows systems Drag whereas company Reaches towards The Cloud (And Blockchain) | killexams.com Real Questions and Pass4sure dumps

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Blockchain acquired a number of mentions all over IBM’s convention call with analysts after revenue have been released Tuesday (April 16) after the markets closed. synthetic intelligence (AI) obtained greater publicity, tied in part to the carrying on with method of embracing the cloud.

when it comes to headline numbers, adjusted income per share got here in at $2.25, beating expectations by way of three pennies.

Revenues ignored expectations, at $18.2 billion; the street had been attempting to find $18.5 billion. The latest excellent-line tally marked a 5 percent decline from a 12 months in the past, and continued the reconfiguring of certain enterprise traces and outright income of different units. by means of illustration, all over the quarter, the tech large sold its loan servicing company to Mr. Cooper community. And in the newest quarter, IBM changed the way it reviews consequences – what turned into once the know-how functions & Cloud structures segment is now the Cloud & Cognitive application and international expertise features phase.

The world expertise services phase – tied to tech infrastructure and support of that infrastructure – saw revenues of $6.9 billion, down 7 p.c. enterprise functions had been $four.1 billion, roughly flat. systems earnings slipped 11 % as mainframe and other hardware demand slid, and demand appeared delicate in emerging markets. CFO James Kavanaugh pointed to the Asia Pacific (AP) place, which noticed income deceleration.

The cloud and cognitive application effects, which center of attention on cloud statistics and transactions, also saw revenue declines, albeit greater muted, with $5 billion in sales, off 1.5 p.c year on 12 months. The enterprise doesn't break out the profits contribution of efforts that are driven by using and embrace AI or blockchain.

become independent from the call and the newest results, during the past it has been mentioned that IBM has filed for a couple of blockchain-linked patents. for instance, the enterprise spoke of it has worked with the transport firm Maersk to assist streamline overseas logistics. The collaboration, called TradeLens, makes use of wise contracts to handle expenses of lading and other documentation used in freight. before that announcement, IBM noted it is working with Walmart to enrich the latter’s meals business provide chain.

In extra element on first-quarter consequences, Kavanaugh stated the enterprise in familiar had “effective performance in offerings that support customers with their digital transformation and journeys to cloud.” Cloud boom accelerated to 12 p.c, measured in steady foreign money, referred to the government.

Cloud revenues are at $19.5 billion during the previous yr, observed administration. application income have been driven by way of hybrid cloud items (with information and AI structures), protection and answer and areas like deliver chain and Watson fitness.

“These offerings are increasingly being infused with AI and then transaction processing systems, together with the middleware and database utility that supports their clients’ mission-essential workloads,” observed Kavanaugh.

management stated that in terms of concrete examples of demand for cloud and company automation – and as is germane to funds – one European tax authority is using the enterprise’s digital enterprise automation platform to “re-design their tax strategies around their facts lake and increase the tax payor journey.”

——————————–

newest Insights: 

Our records and analytics group has developed a couple of inventive methodologies and frameworks that measure and benchmark the innovation that’s reshaping the payments and commerce ecosystem. take a look at their April 2019 Unattended Retail file. 

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elements expected to influence IBM This profits Season | killexams.com Real Questions and Pass4sure dumps

international company Machines IBM is decided to report first-quarter fiscal 2019 effects on Apr 16. specifically, the business surpassed the Zacks Consensus Estimate within the trailing four quarters with ordinary beat of 1.39%.

in the final suggested quarter, the company delivered non-GAAP earnings of $four.87 per share, beating the Zacks Consensus Estimate of $4.eighty one per share. despite the fact, earnings per share (EPS) lowered 5.9% from the year-in the past quarter. The 12 months-over-12 months decline in EPS may also be attributed to bigger tax rate.

Revenues of $21.seventy six billion were just about in accordance with the Zacks Consensus Estimate of $21.seventy four billion. although, the figure declined 3.5% on a yr-over-year foundation. At steady foreign money (cc), revenues dipped 1%. The yr-over-yr decline can primarily be attributed to forex fluctuation and headwinds from IBM Z product cycle.

tips & Estimates

The Zacks Consensus Estimate for the to-be-stated quarter is pegged at $18.sixty five billion, down roughly 2.2% from the 12 months-in the past quarter. additional, the consensus mark for salary is pegged at $2.22 per share, indicating year-over-yr decline of 9.4%.

IBM expects non-GAAP EPS forecast for 2019 to be at least $13.90. The Zacks Consensus Estimate for income is pegged at $13.ninety one per share, up marginally 7% yr over year.

Let’s see how issues are shaping up earlier than this announcement.

foreign company Machines service provider rate and EPS surprise

 

overseas enterprise Machines enterprise expense and EPS shock | overseas business Machines service provider Quote

components to agree with

IBM’s initiatives in blockchain, cloud and business AI market through product rollouts and strategic offers bode smartly. furthermore, the company’s enhancing clout within the cloud, protection and analytics is still a tailwind.

in the fourth quarter, Strategic Imperatives (cloud, analytics, mobility and protection) grew 5% from the 12 months-in the past quarter to $11.5 billion.

Cloud revenues surged 6% from the 12 months-ago quarter to $5.7 billion and 19% (aside from IBM Z product cycle influence).

Coming to techniques, the phase includes both hardware and working programs software revenues. extensive-primarily based adoption of the z14 mainframe, vigor systems and robust flash sales stay key catalysts for the phase.

methods revenues decreased 21% on a year-over-12 months foundation (down 20% at cc) to $2.6 billion, basically because of affect of the IBM Z product cycle. Segmental revenues bearing on Strategic Imperatives plunged 22%, whereas Cloud revenues declined 31%.

IBM Z revenues reduced forty four% 12 months over yr. although, MIPS capacity has expanded round 20%, driven through large-based adoption of the z14 mainframe.

Cognitive solutions’ revenues-exterior increased 2% 12 months over yr (on cc basis) to $5.5 billion. Revenues from Cognitive solutions (together with options application and transaction processing) increased essentially as a result of boom in options utility, together with analytics and artificial intelligence (AI). The Zacks Consensus Estimate for the primary quarter is at present pegged at $four.14 billion.

Revenues from world enterprise features-external section have been $four.three billion, up 4% from the yr-ago quarter (up 6% at cc). The yr-over-yr raise was essentially due to boom throughout all three company areas specifically consulting, application management and international manner functions. The Zacks Consensus Estimate for the first quarter is pegged at $4.23 billion.

Revenues from know-how features & Cloud platforms-external lowered three% from the 12 months-ago quarter (flat at cc) to $8.9 billion. The Zacks Consensus Estimate for the primary quarter is pegged at $8.27 billion.

Product Rollouts & Strategic deals: Key Catalysts

IBM these days completed the launch of its subsequent generation POWER9 processors for mid-range and excessive-conclusion techniques. These are designed for managing superior analytics, cloud environments and data-intensive workloads in AI, HANA, and UNIX markets.

The enterprise additionally introduced new choices optimizing each hardware and software for AI. administration believes that products like PowerAI vision and PowerAI enterprise will support pressure new customer adoption.

Story continues

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

IBM also announced that IBM Cloud can be utilized via RemoteMyApp, a online game streaming startup based mostly out of Poland. IBM Cloud will support RemoteMyApp to extend international market attain and increase its flagship gaming platform, Vortex.

The effective growth in cloud gaming market and extending focus on video online game streaming systems presents gigantic increase chance for IBM.

additional, IBM cloud is witnessing increasing adoption throughout companies based in Europe as evident from this deal. above all, IBM Cloud offers native eu-primarily based aid to its clientele wherein access can also be confined to completely eu-primarily based IBM personnel. These components are helping the tech massive to expand foothold in Europe

IBM is also benefiting from superb client wins, above all in the sports area of late. IBM is introducing several initiatives to bolster journey of the entities concerned in sports leagues, from players, coaches to lovers and management.

The business’s bid to integrate AI into sports programs’ is in sync with its method to head beyond fan engagement to influencing gamers with strong thoughts. These imaginitive solutions aided by means of IBM’s Watson positions the enterprise well in gaining momentum.

IBM is also gaining momentum in blockchain expertise. The traction witnessed by means of the enterprise’s Watson adverts providing is bolstering the exact line, in turn assisting IBM to more suitable compete towards peers.

whereas these trends are anticipated to positively affect effects, IBM’s continuing investments and pricing power concerning its legacy hardware company and ballooning debt stages stay headwinds.

What the Zacks model Unveils

in accordance with the Zacks mannequin, an organization with a Zacks Rank #1 (effective buy), 2 (purchase) or 3 (dangle) has a superb probability of beating estimates if it additionally has a favorable earnings ESP. The promote-rated shares (Zacks Rank #4 or 5) are most beneficial avoided.

IBMhas a Zacks Rank #three and an salary ESP of 0.00%. that you may uncover the top-rated stocks to purchase or sell earlier than they’re stated with our profits ESP Filter.

stocks with Favorable combination

listed here are a couple of stocks which are price due to the fact as their model shows that these have the correct mixture of aspects to carry an revenue beat in the upcoming releases.

The progressive organization PGR has an revenue ESP of +0.09% and a Zacks Rank #2.you can see the comprehensive list of nowadays’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 income ESP of +0.44% and a Zacks Rank #three.

Radical New expertise Creates $12.3 Trillion probability

think about buying Microsoft stock in the early days of personal computers… or Motorola after it released the world’s first cell phone. These technologies changed their lives and created large gains for traders.

today, we’re close to the next quantum bounce in expertise. 7 ingenious corporations are leading this “4th Industrial Revolution” - and early traders stand to earn the largest gains.

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need the newest suggestions from Zacks funding analysis? today, that you may down load 7 top-rated stocks for the subsequent 30 Days. click on to get this free document international enterprise Machines company (IBM) : Free inventory evaluation document Netflix, Inc. (NFLX) : Free stock evaluation document RLI Corp. (RLI) : Free stock evaluation document The innovative employer (PGR) : Free stock evaluation record To examine this article on Zacks.com click here. Zacks funding research


IBM Improves IT Operations with artificial Intelligence | killexams.com Real Questions and Pass4sure dumps

synthetic Intelligence in IT these days

Many IT departments have implemented utility solutions that go beyond standard transaction and analytical processing. These applications comprise fashions that describe definite data behaviors, and these models consume latest records to look if these patterns of facts habits exist. in that case, operational systems can use this counsel to make decisions. a pretty good example of this is fraud detection. IT information engineers use analytics on historical records to examine when fraud happened, code this into a model, and install the mannequin as a provider. Then, any operational system can invoke the mannequin, flow it current facts and acquire a mannequin “rating” that represents the likelihood that a transaction could be fraudulent.

The widely wide-spread time period for these new programs is artificial intelligence (AI). They consist of a mixture of search, optimization and analytics algorithms, statistical evaluation ideas and template processes for ingesting statistics, executing these ideas and making the effects purchasable as functions referred to as fashions. The subset of AI that offers with mannequin introduction and implementation is occasionally known as laptop gaining knowledge of (ML).

machine getting to know and artificial Intelligence

IT departments enforce ML and AI options in the broader context of their facts and processing footprint. here's continually depicted as the following 4-layer hierarchy.

Layer 1: The information.

this layer incorporates the facts distributed throughout the business. It contains mainframe and allotted facts comparable to product and earnings databases, transactional facts and analytical statistics in the statistics warehouse and any big records functions. It additionally may consist of client, seller and organization statistics, perhaps at far flung sites, and even extends to public facts comparable to twitter, news feeds and survey outcomes. an additional possible supply of information is server efficiency logs that encompass aid usage background.

observe that these records exist throughout distinctive hardware systems together with on-premises and cloud-based. As such, quite a few statistics features can exist in distinct kinds and formats (e.g. textual content, ASCII, EBCDIC, UTF-8, XML, photos, audio clips, and so forth.). furthermore, at this stage will exist hardware and utility that manage the facts, including excessive-pace statistics loaders, facts purge and archive tactics, post-and-subscribe approaches for data replication, as well as these for normal backup and restoration and disaster healing planning.

Layer 2: The Analytics Engines.

during this layer exist a mix of hardware and software that executes enterprise analytics towards the records layer. There are a couple of typical avid gamers during this house. They encompass:

  • The IBM Db2 Analytics Accelerator (IDAA) than can also be implemented as standalone hardware or totally built-in inside certain z14 servers;
  • Spark on z/OS;
  • Spark Anaconda on z/OS;
  • Spark clusters on dispensed systems.
  • simply as the records layer happens throughout distinctive hardware systems and dispensed websites, so will the analytics engines layer. The most important function of this accretion is to give an optimized records entry layer in opposition t the underlying information as a service for AI and operational functions.

    Layer three: The laptop researching Platform.

    IT implements computer learning software in this layer. It accesses the data through one or extra of the analytics engines. it's in this layer that IBM promises its newest offering, Watson desktop researching for z/OS (WMLz). WMLz offers a primary desktop getting to know workflow which include right here steps:

  • information Ingestion and training — Inputting records, filling in lacking values, encoding category information, creating indexes and normalizing numeric values;
  • model building and practising — An interface for the records scientist to create a model of information habits in response to old analytics, educate the model to detect facts patterns and validate the mannequin;
  • model Deployment — put into effect the model as a construction system, including methods for updating models in-location and monitoring model effects;
  • feedback Loops — methods that allow automatic model getting to know through feeding model results back into the model training system to update models or produce new ones.
  • records scientists be aware of that one of the crucial highest quality merits of desktop discovering is to make use of the outcomes in operational programs; for example, having an ML mannequin analyze fiscal facts to determine the probability of fraud. This means that you're going to obtain top-quality efficiency in case you set up ML in the hardware ambiance where transaction processing happens. for a lot of big organizations this skill the IBM zServer ambiance.

    Layer 4: computing device researching options.

    Now that they have the laptop discovering platform purchasable as ML functions, they are able to create mixed AI/ML options that invoke these capabilities. IBM has a couple of capable-made solutions for this deposit, together with right here:

  • Db2 AI for z/OS (Db2ZAI) -- the use of Db2 SMF facts for evaluation, Db2ZAI displays and analyzes Db2 operations in a Z/OS atmosphere. it will probably deliver superior question access direction suggestions to the Db2 optimizer to raise SQL performance, diagnose Db2 efficiency abnormalities and advocate corrective action and detect Db2 records anomalies and supply performance tuning concepts;
  • IBM Z Operations Analytics (IZOA) -- This product analyzes z/OS SMF data and detects alterations in subsystem use and forecasts alterations that can be required in the future, does automatic issue analysis and gives issue insights from regular difficulty signatures.
  • Watson computing device discovering on Z

    Let’s take a deeper dive into how Watson laptop learning on Z (WMLz) works and what capabilities it can provide.

    Key efficiency indications (KPIs). WMLz does not inherently understand what performance elements are crucial to you. however, as soon as these KPIs are defined (either via a person or by way of implementing one of the computer discovering solutions stated above), WMLz can analyze KPI records to seek correlations. for example, when one KPI (say, I/O against a critical database) goes up, an extra KPI (say CPU utilization) may go up as smartly. As one more instance, a number of KPIs can be behaviorally similar, so WMLz can cluster them as a bunch and function further analysis across agencies. WMLz can additionally examine KPI baseline behaviors in line with time-of-day, time zone of transactions or seasonal activity.

    Anomaly Detection. as soon as correlations are discovered, WMLz can seem to be opposite outcomes and report them as anomalies. In their I/O example above, an anomaly would be mentioned if I/O in opposition t a important database expanded however CPU utilization diminished.

    sample awareness. As with many machine gaining knowledge of engines, WMLz will look for patterns amongst KPIs and facts identifiers. for example, CPU might also increase when processing definite categories of transactions.

    KPI prediction. An extension of fundamental KPI processing, WMLz can use the past behaviors of groups of KPIs to foretell the future. accept as true with their I/O illustration once once more. The product might also realize that definite transactions become more a large number of all over a selected time duration, and these transactions devour drastically extra CPU cycles. The product may additionally then predict future CPU spikes.

    Batch workload evaluation. Many IT retail outlets have a huge contingent of batch processing it's tightly scheduled and comprises job and aid dependencies. Some jobs ought to wait for his or her predecessors to comprehensive, some use massive shared elements (corresponding to tape drives or strong point hardware) and some are so resource-intensive that then can't be accomplished at the equal time. WMLz can analyze the workload information, together with aid usage, and supply recommendations for balancing materials or tuning elapsed instances.

    MLC can charge sample analysis and price discount. Some IBM utility license prices are billed monthly, and the license amount might also depend upon optimum CPU usage throughout peak periods. WMLz can analyze CPU usage throughout time, search for patterns and make predictions and recommendations for utility license cost discount.

    Watson computing device studying for z/OS — elements

    IBM’s Watson desktop gaining knowledge of for z/OS permits IT its option of building environments to strengthen models together with IBM SPSS Modeler. These environments support records scientists by using notebooks, statistics visualization tools and wizards to speed the construction method. a couple of short-beginning application templates are additionally integrated within the toolset for common company necessities equivalent to fraud detection, load approval and IT operational analytics. The newest version of WMLz (edition 2.1.0) includes assist for Ubuntu Linux on Z, java APIs, simplified Python equipment administration and a number of other elements.

    fascinated readers should reference the hyperlinks under for greater distinct technical information.

    # # #

    See all articles with the aid of Lockwood Lyon

    REFERENCES

    computer learning and synthetic Intelligencehttps://en.wikipedia.org/wiki/Machine_learning

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

    the use of Anaconda with Spark — Anaconda 2.0 documentationhttps://medical doctors.anaconda.com/anaconda-scale/spark/

    Watson computing device discovering - Overviewhttps://www.ibm.com/cloud/computer-researching

    Watson desktop researching - Resourceshttps://www.ibm.com/cloud/machine-researching/resources


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    Disney In One Buy: How Disney Is Unifying Its Advertising Approach | killexams.com real questions and Pass4sure dumps

    At AdExchanger’s Programmatic I/O conference in San Francisco on April 30, Laura Nelson, Disney’s SVP of advertising solutions and performance advertising, will share how it’s selling connected TV as consumer habits and advertising preferences change.

    Disney is on a mission to unify its inventory across platforms and offer buyers alluring scale against much smaller audience segments.

    In the new Disney platform, buyers will be able to find their target audience across ESPN, ABC and Freeform with a single buy. They can also find larger pools of content. If they want news, for example, they can pair ESPN with ABC News. And just weeks ago, the 21st Century Fox deal closed, adding FX and National Geographic inventory to the group.

    While unification is the goal, the platform is still a work in progress. Disney signed a deal with Google Ad Manager in November 2018 to unify all its inventory across networks. Structurally, Disney is already aligned. Three months before Disney made the move to unify its tech, it added ESPN to the purview of Rita Ferro, who now oversees ad sales from ABC to ESPN.

    Beginning in October at the start of the broadcast year, Disney wants to make it possible to look at connected TV (CTV) inventory across its portfolio, from Apple TV to Roku, and then do campaign delivery across platforms.

    That platform unification won’t include two pieces. The first is Hulu; though Disney now owns a majority stake in Hulu, it can’t access subscriber data to inform buys. Its own content there runs on a completely separate technology stack. The second piece is Disney+. The much-publicized direct-to-consumer streaming service will be available by subscription only, with no ads.

    Laura Nelson, Disney’s SVP of advertising solutions and performance marketing, talked to AdExchanger about Disney’s vision to make it easy for buyers to purchase audience segments across different networks, with the vision of converging digital, CTV and linear.

    AdExchanger: How is Disney making CTV inventory more attractive to buyers?

    LAURA NELSON: Buyers see CTV – which brings the best of digital and linear into one place – as attractive already. They are focused across all their inventory to make sure it’s as targetable as possible. There are sometimes challenges with unlocking that targeting.

    What’s an example of a situation where it’s difficult to unlock that data?

    We have to go platform by platform to do any infrastructure integration between systems. On ABC, the majority of the inventory runs on Hulu. It also runs on Dish Sling. Hulu fully owns its ad stack and the platform, so they don’t have to worry about their distribution deals.

    That’s a good point. How do programmers like Disney secure data to help buyers when a good chunk of the data is held by intermediaries, like distributors, devices or platforms?

    Advanced targeting can go through first-, second- or third-party data. Clients increasingly want to transact against their first-party data, so they want to enable that. They have relationships with LiveRamp or Oracle’s BlueKai to unlock audience segments. There is a third bucket, where they have a team focused on creating first-party audience segments that are different across Disney affinity brands as well as their network.

    What goes into Disney’s first-party data offering?

    We take learnings about audiences tied to their inventory and match it with first-party data so they can more accurately target and assess behavior segments. That’s a big focus around CTV. They have a team to make sure this machine learning extends to all platforms [desktop, mobile app] including CTV.

    It’s a predictive model their data science team created that looks at viewership information from mobile games, inventory on their own platforms as well as [viewership, not subscription] information from Hulu. And they add surveys to create audience segments and predict that this person is more likely to buy X, Y or Z. It’s not necessarily one-to-one targeting. We’re making sure we’re doing this in a privacy-safe way.

    What’s an example of how Disney inventory will be unified as you migrate to Google Ad Manager?

    We will have the ability to move inventory across different brands with the creation of one deal ID. On the direct side, [it’s] the ability to buy inventory across ABC and Freeform. It was different setups before. When you bring inventory types together, like ESPN and ABC News, you think about how you could bring those live news or short-form video content types together and sell against an audience. In silos, the scale wasn’t there. You need scale for an audience-based buy.

    What’s different about the way you see buyers asking to transact today?

    Historically, there’s been the divide between the traditional linear buyer and digital buyer. But in the premium content space, agencies and clients understand that you need to buy inventory across platforms, not separately. There is a move across all agencies to figure out how to transact in this manner. Getting to a place where you are buying audiences across platforms is the future. They need to agree on measurement to make that happen.

    What are the biggest gaps today in terms of data?

    There is not a standard device ID across OTT devices. And it’s difficult to identify the identifier on OTT devices. Everyone works with them in a different way, and there are differences in how and where you integrate with those platforms.

    We’re trying to figure out how to create proxies for that OTT viewing, and how to measure or target against that [proxy]. They know there is promise, but there is not unification in how you’re identifying those users. 

    This interview has been edited and condensed.


    IBM Improves IT Operations with Artificial Intelligence | killexams.com real questions and Pass4sure dumps

    Artificial Intelligence in IT Today

    Many IT departments have implemented software solutions that go beyond simple transaction and analytical processing. These packages contain models that describe certain data behaviors, and these models consume current data to see if these patterns of data behavior exist. If so, operational systems can use this information to make decisions. A good example of this is fraud detection. IT data engineers use 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 be fraudulent.

    The general 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 also may include customer, vendor and supplier data, perhaps at remote sites, and even extends to public data such as twitter, news feeds and survey results. Another possible source of data is server performance logs that include 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 level 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 disaster recovery planning.

    Layer 2: The Analytics Engines.

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

  • The IBM Db2 Analytics Accelerator (IDAA) than can be implemented as standalone hardware or fully integrated within certain 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 function 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 Building 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 produce new ones.
  • Data scientists know that one of the greatest benefits of machine learning is to use the results in operational systems; for example, having an ML model analyze financial 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 large organizations this means the IBM zServer environment.

    Layer 4: Machine Learning Solutions.

    Now that they have 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 increase 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 use and forecasts changes that may be required in the future, does automatic problem analysis and provides problem insights from known problem signatures.
  • Watson Machine Learning on Z

    Let’s take 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 important to you. However, once these KPIs are defined (either by a user or by implementing one of the machine learning solutions noted above), WMLz can analyze KPI data to look for correlations. For example, when one KPI (say, I/O against a critical database) goes up, another KPI (say CPU usage) may go up as well. As another example, several KPIs may be behaviorally similar, so WMLz can cluster them as a group and perform further analysis across groups. WMLz can also determine KPI baseline behaviors based on time-of-day, time zone of transactions or seasonal activity.

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

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

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

    Batch workload analysis. Many IT shops have a large contingent of batch processing that is tightly scheduled and includes job and resource dependencies. Some jobs must wait for their predecessors to complete, some use significant shared resources (such as tape drives or specialty hardware) and some are so resource-intensive that then cannot be executed at the same time. WMLz can analyze 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 analyze CPU usage across time, look for patterns and make 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 choice of development environments to develop models including IBM SPSS Modeler. These environments assist data scientists by using notebooks, data visualization tools and wizards to speed the development process. Several quick-start application templates are also incorporated in the toolset for common business requirements such as fraud detection, load approval and IT operational analytics. The latest version of WMLz (version 2.1.0) includes support 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 all 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


    Wireless Connectivity Market Updated Research 2023: Major Drivers, Key Trends and Emerging Opportunities Forecast 2023 | killexams.com real questions and Pass4sure dumps

    Apr 18, 2019 (The Expresswire via COMTEX) -- In this report, global Wireless Connectivity market by type, application, region and manufacturer and forecast. For the region, type and application, the sales, revenue and their market share, growth rate are key research objects.

    Wireless Connectivity Marketanalysis report delivers the latest industry data and future trends, letting you to recognize the products and end users which derives the revenue growth and profitability. The Wireless Connectivity report lists the top competitors and delivers the insights strategic industry analysis of the key aspects influencing the market.

    Get Sample Copy of this Report at-https://www.industryresearch.co/enquiry/request-sample/12900119

    Report further studies the Wireless Connectivity market development status and future trend across the world. Also, it splits Wireless Connectivity market by type and by applications to fully and deeply research and reveal market profile and prospects.

    Wireless Connectivity Market by Top Manufacturers:Intel, Qualcomm, NXP Semiconductors, Stmicroelectronics, Texas Instruments, Microchip Technology, Mediatek, Cypress Semiconductor, Broadcom, Enocean, Nexcom International Co., Ltd., Skyworks Solutions, Inc., Murata Manufacturing Co., Ltd., Marvell Technology Group Ltd., Quantenna Communications, Inc., Renesas Electronics Corporation, Nordic Semiconductor, Ceva, Inc., Espressif Systems Pte., Ltd., Peraso Technologies, Inc.

    By Connectivity TechnologyWi-Fi, Bluetooth Classic, Bluetooth Smart, Bluetooth Smart Ready, Z-Wave, ZigBee, Near Field Communication (NFC), GPS/GNSS, Others (include N-Wave, Wi-SUN, Extended Coverage€“GSM€“Internet of Things (EC-GSM-IoT), Weightless, Ingenu, Qowisio, Wi-Fi HaLow, Wireless Highway Addressable Remote Transducer Protocol (WirelessHART), and Telensa.)

    By TypeWireless Local Area Network (WLAN), Wireless Personal Area Network (WPAN), Satellite (GNSS), Low-Power Wide-Area Network (LPWAN), Cellular,

    Inquire more and share questions if any before the purchase on this report at-https://www.industryresearch.co/enquiry/pre-order-enquiry/12900119

    Geographically, this report is segmented into several key regions, with sales, revenue, market share and growth Rate of Wireless Connectivity in these regions, from 2014 to 2024, covering

  • North America (United States, Canada and Mexico)
  • Europe (Germany, UK, France, Italy, Russia and Turkey etc.)
  • Asia-Pacific (China, Japan, Korea, India, Australia, Indonesia, Thailand, Philippines, Malaysia and Vietnam)
  • South America (Brazil etc.)
  • Middle East and Africa (Egypt and GCC Countries)
  • Report Price: $ 3500 (Single-User License)

    Purchase Wireless Connectivity Market Report Here-https://www.industryresearch.co/purchase/12900119

    Detailed TOC of 2018-2023 Global and Regional Wireless Connectivity Industry Production, Sales and Consumption Status and Prospects Professional Market Research Report

    Chapter 1 Industry Overview

    1.1 Definition

    1.2 Brief Introduction by Major Type

    1.3 Brief Introduction by Major Application

    Chapter 2 Production Market Analysis

    2.1 Global Production Market Analysis

    2.2 Regional Production Market Analysis

    Chapter 3 Sales Market Analysis

    3.1 Global Sales Market Analysis

    3.2 Regional Sales Market Analysis

    Chapter 4 Consumption Market Analysis

    4.1 Global Consumption Market Analysis

    4.2 Regional Consumption Market Analysis

    Chapter 5 Production, Sales and Consumption Market Comparison Analysis

    5.1 Global Production, Sales and Consumption Market Comparison Analysis

    5.2 Regional Production, Sales Volume and Consumption Volume Market Comparison Analysis

    Chapter 6 Major Manufacturers Production and Sales Market Comparison Analysis

    6.1 Global Major Manufacturers Production and Sales Market Comparison Analysis

    6.2 Regional Major Manufacturers Production and Sales Market Comparison Analysis

    Chapter 7 Major Type Analysis

    Chapter 8 Major Application Analysis

    Chapter 9 Industry Chain Analysis

    9.1 Up Stream Industries Analysis

    9.1.1 Raw Material and Suppliers

    9.1.2 Equipment and Suppliers

    9.2 Manufacturing Analysis

    9.2.1 Manufacturing Process

    9.2.2 Manufacturing Cost Structure

    9.2.3 Manufacturing Plants Distribution Analysis

    9.3 Industry Chain Structure Analysis

    Chapter 10 Global and Regional Market Forecast

    10.1 Production Market Forecast

    10.1.1 Global Market Forecast

    10.1.2 Major Region Forecast

    10.2 Sales Market Forecast

    10.2.1 Global Market Forecast

    10.2.2 Major Classification Forecast

    10.3 Consumption Market Forecast

    10.3.1 Global Market Forecast

    10.3.2 Major Region Forecast

    10.3.3 Major Application Forecast

    Chapter 11 Major Manufacturers Analysis

    11.1 Company 3

    11.1.1 Company Introduction

    11.1.2 Product Specification and Major Types Analysis

    11.1.3 2012-2017 Production Market Performance

    11.1.4 2012-2017 Sales Market Performance

    11.1.5 Contact Information

    11.2 Company 2

    11.2.1 Company Introduction

    11.2.2 Product Specification and Major Types Analysis

    11.2.3 2012-2017 Production Market Performance

    11.2.4 2012-2017 Sales Market Performance

    11.2.5 Contact Information

    11.3 Company 3

    11.3.1 Company Introduction

    11.3.2 Product Specification and Major Types Analysis

    11.3.3 2012-2017 Production Market Performance

    11.3.4 2012-2017 Sales Market Performance

    11.3.5 Contact Information

    Chapter 12 New Project Investment Feasibility Analysis

    12.1 New Project SWOT Analysis

    12.2 New Project Investment Feasibility Analysis

    About Us: -

    Industry Research is an upscale platform to help key personnel in the business world in strategizing and taking visionary decisions based on facts and figures derived from in depth market research. They are one of the top report resellers in the market, dedicated towards bringing you an ingenious concoction of data parameters.

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    Press Release Distributed by The Express Wire

    To view the original version on The Express Wire visit Wireless Connectivity Market Updated Research 2023: Major Drivers, Key Trends and Emerging Opportunities Forecast 2023



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    References :


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