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C2090-603 IBM System z Solution Sales V6

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

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IBM IBM System z Solution

The AI Eye: IBM (NYSE: IBM) Watson fitness Lists 15 correct 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 news 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

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IBM (NYSE:IBM) Watson fitness published its 15 proper health programs annual analyze 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, Mercy 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 care across dissimilar facilities and the alternatives that can come with scale. The associations recognized in the IBM Watson fitness 15 appropriate fitness methods study are proposing a blueprint for a way to increase first-rate, decrease fees and achieve surprising patient pride on a consistent foundation."

exceptional Actimize, a subsidiary of fine Ltd. (NasdaqGS:exceptional), has enhanced its intelligent eCommunications Surveillance solution with subsequent generation desktop researching capabilities, clever analytics and investigative tools to strengthen surveillance detection and case administration, reduce false signals, and power down compliance charges, based on a press release these days. Chris Wooten, government vice president, quality 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 whole 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 reach three,228 million via 2030. This boom is credited to the development 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 push 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 also makes it possible for clients to monitor their fitness conditions in real-time in a price-beneficial manner, promotion the use of wise 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 help reflects the mighty momentum for mission essential deployments of the realm's quickest growing to be 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 be database by way of enabling groups to without problems capitalize on the vigor of contemporary applications and information to benefit a aggressive skills," said Dev Ittycheria, president and CEO, MongoDB. "For years, the world's largest organizations have run important purposes on IBM mainframes. Their stream to support IBM z systems is a testomony to their commitment to their users and shoppers to make MongoDB available on all essential structures. With this announcement, companies can now build and run contemporary, mission-crucial purposes on confirmed mainframe technologies."

MongoDB is working carefully with IBM to engineer MongoDB business Server to be 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 mix 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:

-- be a part of the circulate and practice to develop into a MongoDB partner -- MongoDB is hiring! Browse open positions these days -- register for MongoDB college's free online classes -- be part of MongoDB on LinkedIn and follow @MongoDB on Twitter

About MongoDB MongoDB is the subsequent-generation database that helps businesses seriously change their industries by way of harnessing the energy of information. the realm's most sophisticated groups, from chopping-side startups to the biggest businesses, use 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 be 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. All 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 Business Machines IBM is set to report first-quarter fiscal 2019 results on Apr 16. Notably, the company surpassed the Zacks Consensus Estimate in the trailing four quarters with average beat of 1.39%.

In the last reported quarter, the company delivered non-GAAP earnings of $4.87 per share, beating the Zacks Consensus Estimate 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 be attributed to higher tax rate.

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

Guidance & Estimates

The Zacks Consensus Estimate for the to-be-reported quarter is pegged at $18.65 billion, down roughly 2.2% from the year-ago quarter. Further, the consensus mark 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 be at least $13.90. The Zacks Consensus Estimate for earnings is pegged at $13.91 per share, up marginally 7% year over year.

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

International Business Machines Corporation Price and EPS Surprise

 

International Business Machines Corporation Price and EPS Surprise | International Business 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 strong flash 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 Estimate for the first quarter is currently pegged at $4.14 billion.

Revenues from Global Business Services-external segment were $4.3 billion, up 4% from the year-ago quarter (up 6% at cc). The year-over-year increase was primarily due to growth across all three business areas namely consulting, application management and global process services. The Zacks Consensus Estimate 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 Estimate 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 also introduced new offerings optimizing both hardware and software for AI. Management believes that products like PowerAI Vision and PowerAI Enterprise will help 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 Business with digital transformation initiatives.

IBM also announced that IBM Cloud will be utilized by RemoteMyApp, a game streaming startup based out of Poland. IBM Cloud will aid RemoteMyApp to expand global market reach 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 opportunity 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 support to its clientele by which access can be confined to solely EU-based IBM employees. These factors are aiding the tech giant to expand foothold in Europe

IBM is also 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 go 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 also 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 business 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 good chance of beating estimates if it also 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 Favorable Combination

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

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Large businesses don’t have to be lousy innovators

Creative Construction: The DNA of Sustained Innovation

by Gary P. Pisano, PublicAffairs, 2019

Gary Pisano doesn’t buy the idea that large enterprises are inherently lousy innovators. Back in 2006, Pisano, the Harry E. Figgie Professor of Business Administration at Harvard Business School, traced the origin of every drug approved by the FDA over a 20-year period 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 dead heat” — R&D productivity was no better in the smaller biotechs than in big pharma.

Pisano also points to anecdotal evidence to support his opposition to the conventional wisdom about innovation in large 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 old 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 difference between a Blockbuster and an IBM is the ability of leaders to sustain and rejuvenate the innovation capacity of their companies. It’s an ability he calls “creative construction,” and he writes that it “requires a delicate balance of exploiting existing resources and capabilities without becoming imprisoned by them.”

Walking that tightrope is a challenge for large companies. It’s tough to move 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 large organizations can also be daunting. “When you get to be the scale of a J&J, you have a lot of moving parts,” he explains. “You now have a system with serious frictions. Friction impedes mobility. Lack of mobility means lack of innovation.”

But large companies also have some advantages that can give them a leg up in innovation. “Larger enterprises like J&J have massive financial resources to explore new opportunities,” says Pisano. They can hedge their bets, tap deep reservoirs of talent, navigate regulatory agencies, and use their huge distribution networks and strong brands to roll out new products to millions of existing customers.

So how do leaders of large companies meet the challenges and leverage the benefits of innovation? Pisano’s answer 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) building an innovation culture.”

Bromides, such as “eat your own lunch before someone else does,” are the bane of innovation strategy, according to Pisano. Take IBM’s mainframe business: “Eat your own lunch” suggests that IBM should have cannibalized its success and jumped wholeheartedly into the PC business 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 all 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 need to map and clarify their options for both product and business-model innovation. Then, they must allocate 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 need to perform three basic tasks: (1) search for novel and valuable problems and solutions, (2) synthesis of diverse streams of ideas into coherent business concepts, and (3) selection among opportunities.”

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

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

Pisano’s three-part plan 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 argue that none of the capabilities upon which the long-term success of large enterprises depends are particularly earthshaking. Instead, they must be comprehensively imagined and rigorously pursued. In Creative Construction, Pisano does an uncommonly good 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 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



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