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000-M19 IBM Information Management DB2 9 Warehouse Technical Sales Mastery Test v1

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000-M19 exam Dumps Source : IBM Information Management DB2 9 Warehouse Technical Sales Mastery Test v1

Test Code : 000-M19
Test title : IBM Information Management DB2 9 Warehouse Technical Sales Mastery Test v1
Vendor title : IBM
real questions : 43 true Questions

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IBM IBM Information Management DB2

IBM Db2 query Optimization using AI | killexams.com true Questions and Pass4sure dumps

In September 2018, IBM announced a novel product, IBM Db2 AI for z/OS. This synthetic intelligence engine displays records access patterns from executing SQL statements, makes expend of computer researching algorithms to elect highest trait patterns and passes this information to the Db2 query optimizer to subsist used by means of subsequent statements.

laptop gaining information of on the IBM z Platform

In may of 2018, IBM introduced edition 1.2 of its machine learning for z/OS (MLz) product. here is a hybrid zServer and cloud application suite that ingests efficiency information, analyzes and builds fashions that signify the fitness fame of a variety of indications, screens them over time and gives precise-time scoring capabilities.

a few elements of this product offering are geared toward helping a group of mannequin builders and managers. as an instance:

  • It helps dissimilar programming languages equivalent to Python, Scala and R. This enables information modelers and scientists to produce expend of a language with which they are prevalent;
  • A graphical person interface known as the visual mannequin Builder guides model builders with out requiring extremely-technical programming advantage;
  • It includes diverse dashboards for monitoring mannequin results and scoring services, as well as controlling the device configuration.
  • This computing device getting to know suite become firstly geared toward zServer-primarily based analytics purposes. one of the most first evident selections became zSystem performance monitoring and tuning. gadget management Facility (SMF) records which are automatically generated through the operating tackle deliver the uncooked statistics for tackle resource consumption such as relevant processor usage, I/O processing, reminiscence paging and the like. IBM MLz can bring together and shop these statistics over time, and construct and train models of device habits, rating these behaviors, identify patterns no longer effortlessly foreseen by way of people, better key performance symptoms (KPIs) and then feed the mannequin effects back into the system to absorb an effect on device configuration alterations that may expand efficiency.

    The next step turned into to setaside in coerce this suite to research Db2 performance statistics. One answer, known as the IBM Db2 IT Operational Analytics (Db2 ITOA) reply template, applies the machine gaining information of technology to Db2 operational information to profit an figuring out of Db2 subsystem fitness. it may possibly dynamically construct baselines for key efficiency warning signs, provide a dashboard of those KPIs and provides operational group of workers actual-time insight into Db2 operations.

    while ordinary Db2 subsystem efficiency is a vital component in gauge utility health and performance, IBM estimates that the DBA guide corpse of workers spends 25% or more of its time, " ... fighting entry direction issues which reason efficiency degradation and service affect.". (See Reference 1).

    AI comes to Db2

    consider the plight of concomitant DBAs in a Db2 ambiance. In ultra-modern IT world they must aid one or more massive facts functions, cloud utility and database functions, utility installing and configuration, Db2 subsystem and application performance tuning, database definition and management, calamity healing planning, and greater. query tuning has been in existence considering the origins of the database, and DBAs are usually tasked with this as well.

    The coronary heart of question direction evaluation in Db2 is the Optimizer. It accepts SQL statements from applications, verifies authority to access the information, studies the areas of the objects to subsist accessed and develops an inventory of candidate information access paths. These access paths can involve indexes, table scans, a variety of desk subsist a allotment of methods and others. in the data warehouse and sizable information environments there are continually further choices purchasable. One of those is the existence of summary tables (once in a while referred to as materialized question tables) that hold pre-summarized or aggregated facts, as a consequence enabling Db2 to avoid re-aggregation processing. an additional option is the starjoin access path, middling in the information warehouse, the dwelling the order of table joins is modified for performance causes.

    The Optimizer then stories the candidate access paths and chooses the access path, "with the bottom cost." imbue in this context capability a weighted summation of resource utilization including CPU, I/O, reminiscence and other elements. finally, the Optimizer takes the lowest cost access path, stores it in reminiscence (and, optionally, within the Db2 directory) and starts access course execution.

    big records and facts warehouse operations now encompass software suites that permit the enterprise analyst to produce expend of a graphical interface to build and manipulate a miniature records model of the data they want to analyze. The packages then generate SQL statements in accordance with the users’ requests.

    The issue for the DBA

    as a way to execute first rate analytics to your diverse records shops you want an outstanding realizing of the records requirements, an realizing of the analytical features and algorithms obtainable and a high-efficiency data infrastructure. lamentably, the quantity and placement of facts sources is increasing (both in dimension and in geography), statistics sizes are turning out to be, and functions proceed to proliferate in number and complexity. How should IT managers champion this atmosphere, above eachandevery with probably the most skilled and age personnel nearing retirement?

    consider additionally that a huge a allotment of reducing the entire cost of ownership of those techniques is to derive Db2 functions to dash quicker and more successfully. This continually translates into using fewer CPU cycles, doing fewer I/Os and transporting much less facts across the community. seeing that it's regularly complicated to even establish which applications could profit from efficiency tuning, one strategy is to automate the detection and correction of tuning concerns. here is the dwelling computing device getting to know and synthetic intelligence may too subsist used to wonderful impact.

    Db2 12 for z/OS and synthetic Intelligence

    Db2 edition 12 on z/OS makes expend of the computer studying facilities mentioned above to collect and preserve SQL query textual content and entry route particulars, as well as specific performance-related historic assistance corresponding to CPU time used, elapsed instances and result set sizes. This offering, described as Db2 AI for z/OS, analyzes and shops the facts in desktop getting to know fashions, with the model analysis effects then being scored and made attainable to the Db2 Optimizer. The subsequent time a scored SQL statement is encountered, the Optimizer can then expend the model scoring facts as enter to its entry path option algorithm.

    The outcomes may still subsist a discount in CPU consumption as the Optimizer uses mannequin scoring input to elect better entry paths. This then lowers CPU prices and speeds software response instances. a sizable talents is that using AI software doesn't require the DBA to absorb facts science competencies or deep insights into query tuning methodologies. The Optimizer now chooses the optimum access paths based no longer most effectual on SQL question syntax and information distribution facts but on modelled and scored ancient performance.

    This may too subsist exceptionally crucial if you store facts in numerous places. as an instance, many analytical queries against sizable records require concurrent access to unavoidable statistics warehouse tables. These tables are frequently called dimension tables, and that they comprise the information points constantly used to wield subsetting and aggregation. as an instance, in a retail environment dependence a desk called StoreLocation that enumerates each store and its dwelling code. Queries in opposition t preserve revenue statistics may additionally requisite to aggregate or summarize income by means of region; therefore, the StoreLocation table might subsist used by some huge information queries. during this environment it is middling to acquire the dimension tables and replica them regularly to the sizable data software. in the IBM world this dwelling is the IBM Db2 Analytics Accelerator (IDAA).

    Now deem about SQL queries from both operational purposes, records warehouse clients and massive information traffic analysts. From Db2's perspective, eachandevery these queries are equal, and are forwarded to the Optimizer. youngsters, within the case of operational queries and warehouse queries they should undoubtedly subsist directed to access the StoreLocation desk within the warehouse. nevertheless, the query from the company analyst against massive records tables should probably entry the replica of the desk there. This consequences in a proliferations of information access paths, and extra toil for the Optimizer. fortunately, Db2 AI for z/OS can supply the Optimizer the assistance it should produce sensible entry route choices.

    the way it Works

    The sequence of pursuits in Db2 AI for z/OS (See Reference 2) is often privilege here:

  • all the way through a bind, rebind, prepare or clarify operation, an SQL commentary is passed to the Optimizer;
  • The Optimizer chooses the data access direction; because the alternative is made, Db2 AI captures the SQL syntax, access course alternative and question performance statistics (CPU used, and many others.) and passes it to a "studying assignment";
  • The gaining information of assignment, which will too subsist completed on a zIIP processor (a non-universal-purpose CPU core that does not element into software licensing costs), interfaces with the laptop discovering software (MLz mannequin capabilities) to store this assistance in a model;
  • as the amount of facts in each model grows, the MLz Scoring carrier (which too can subsist finished on a zIIP processor) analyzes the model information and ratings the habits;
  • throughout the next bind, rebind, prepare or explain, the Optimizer now has access to the scoring for SQL fashions, and makes applicable alterations to entry path choices.
  • There are too a number of consumer interfaces that provide the administrator visibility to the repute of the amassed SQL remark performance records and model scoring.

    abstract

    IBM's desktop studying for zOS (MLz) offering is being used to remarkable effect in Db2 version 12 to enhance the efficiency of analytical queries as well as operational queries and their associated functions. This requires management attention, as you should examine that your enterprise is ready to devour these ML and AI conclusions. How will you measure the prices and merits of using laptop getting to know? Which IT guide team of workers absorb to subsist tasked to reviewing the effect of model scoring, and perhaps approving (or overriding) the effects? How will you assessment and warrant the assumptions that the utility makes about access route decisions?

    In other words, how well did you know your information, its distribution, its integrity and your existing and proposed access paths? this will check where the DBAs spend their time in aiding analytics and operational software efficiency.

    # # #

    Reference 1

    John Campbell, IBM Db2 unique EngineerFrom "IBM Db2 AI for z/OS: enhance IBM Db2 software efficiency with machine researching"https://www.worldofdb2.com/routine/ibm-db2-ai-for-z-os-raise-ibm-db2-utility-efficiency-with-ma

    Reference 2

    Db2 AI for z/OShttps://www.ibm.com/support/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html

    See eachandevery articles through Lockwood Lyon


    IBM PowerVM: Product Overview and perception | killexams.com true Questions and Pass4sure dumps

    See the total checklist of pattern server virtualization software.

    final analysis:

    IBM PowerVM can virtualize AIX, IBM Linux, and IBM i valued clientele working on its vigour server platform. certainly, it is likely one of the most full featured virtulization courses on the market – no surprise, given IBM's deep legacy in the statistics center.

    however may additionally now not subsist the least difficult platform to setaside into effect. it'll require specialists to install it. because of this, mid-sized and sizable companies may still execute great, however SMBs can subsist top-rated to remain away from it unless they could absorb the funds for outdoor support. IBM PowerVM is geared notably for brand novel superior information facilities with demanding software workloads.

    Product Description:

    IBM PowerVM can consolidate varied workloads onto fewer methods, expanding server utilization and decreasing can charge. PowerVM provides a secure and scalable server virtualization atmosphere for AIX, IBM i and Linux purposes built upon the RAS aspects of the energy programs platform. in brief, its hypervisor is rather bendy. it may champion supply features in the cloud quicker by way of automating deployment of VMs and storage. it will possibly too champion derive rid of downtime by the expend of are live mobility between servers.

    PowerVM 2.2.6 grants commercial enterprise-grade virtualization, providing the foundation for cloud computing on IBM verve techniques. it could correctly partake components among applications, consolidate distinctive workloads, and provide the utility mobility in a multi-cloud infrastructure. It is declared to raise resource utilization, reduce working fees, and supply a extra agile atmosphere for IBM AIX, IBM i, and IBM Linux purposes running on verve programs.

    in the most concomitant liberate, IBM has greater tightly built-in PowerVM with the energy platform. every POWER9 server comes with POWERVM commercial enterprise version. there is additionally a typical version as well as an IBM PowerVM, Linux version. PowerVM regular version contains privilege here add-ons:

  • Micro-Partitioning expertise
  • N-Port identification Virtualization (NPIV)
  • Partition droop and resume is supported on POWER8 processor-based servers when the firmware is at stage 8.4.0, or later.
  • PowerVM NovaLink
  • Shared processor swimming pools
  • Shared storage pools
  • Single Root I/O Virtualization (SR-IOV)
  • skinny provisioning
  • digital I/O Server (VIOS)
  • virtual network Interface Controller adapters
  • “It has been very respectable with limited to no downtime. they absorb been in a position to stretch their IT greenbacks since the refresh cost on IBM energy can dash for years. also, they now absorb been capable of add many extra VMs to actual machines than different platforms can run,” observed a information core supervisor in manufacturing.

    Servers/operating systems:

    AIX, Linux and IBM i purchasers

    “Our enterprise utilizes VMware and PowerVM. VMware is consumer pleasant and makes supporting home windows OS less difficult. PowerVM is relocating in that course. PowerVM is superior in for you to prioritize workloads across different VMs and subsist granular in your reservation of cores and virtual CPUs. PowerVM lets you adjust VM characteristics while the VM is up and running,” talked about a gadget Admin in oil & gas.

    Implementation:

    PowerVM is a application down load.

    Scalability:

    up to 1000 VMs on a solitary server.

    Overhead:

    10% to 15%

    administration:

    management tackle comparable to Hardware administration Console (HMC), integrated Virtualization supervisor (IVM), and PowerVC champion to aggregate and manipulate materials by using a consolidated logical view. that you can commandeer processors to partitions in increments of 0.01, which permits distinctive partitions to partake the processing power of the equipment. When the firmware is at stage 7.6, or later, micropartitions will too subsist defined as minute as 0.05 of a processor and can subsist changed in increments as minute as 0.01 of a processor. A optimum of 20 micropartitions can subsist created per core.

    A working AIX, Linux, or IBM i logical partition may too subsist suspended along with its operating device and functions. that you could partake reminiscence among partitions in a shared reminiscence pool, by using PowerVM energetic reminiscence Sharing. vigour Virtualization performance (PowerVP) is a efficiency monitoring reply that provides targeted and actual-time assistance about virtualized workloads that are working on verve programs. which you could expend PowerVP to preserve in repartee how virtual workloads expend substances, to research performance bottlenecks, and to produce counseled decisions about aid allocation and virtualized computing device placement.

    Patching/Backup:

    provided through other IBM energy equipment.

    Migration:

    that you would subsist able to migrate an lively or passive AIX, Linux, or IBM i logical partition from one device to yet another through the expend of reside Partition Mobility.

    safety:

    energy systems provide a secured server platform. POWER9 hardware and firmware produce it much more cozy for cloud deployment with key elements for PowerVM servers. Implementation comprises:

  • A at ease IPL technique or secure Boot which handiest allows for platform manufacturer signed Hostboot and power Hypervisor (PHYP) connected firmware up via and together with Partition Firmware (PFW) to dash on the gadget.
  • A framework to aid faraway Attestation of the tackle firmware stack via a hardware depended on Platform Module (TPM).
  • Key Markets:

    Virtualization for AIX, Linux and IBM i shoppers operating IBM verve platforms.

    “It may subsist over engineered for smaller purposes. although, if the infrastructure is in location that you may utilize it to dash Linux VMs as neatly,” observed a system Admin in Oil & fuel

    cost:

    starting at $590 per core, free with any other IBM items.

    Product

    IBM PowerVM

    platforms

    AIX, Linux and IBM i valued clientele

    Scalability

    1000 VMs on a solitary server

    Overhead %

    10 to 15

    Markets

    Virtualization for AIX, Linux and IBM i customers operating IBM power systems

    cost

    $590 per core

    Migration

    flow lively or passive VMs

    Key Differentiator

    best suited for IBM environments

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    Winning the #ArtificialIntelligence War | @ExpoDX #IoT #DigitalTransformation | killexams.com true questions and Pass4sure dumps

    There is a war a-brewin’, but this war will subsist fought with wits and not animal strength. Ever since Russian President Vladimir Putin’s declaration that “the nation that leads in AI (Artificial Intelligence) will subsist the ruler of the world,” the press and analysts absorb created hysteria regarding the ramifications of synthetic intelligence on everything from public education to unemployment to healthcare to Skynet.

    Note: synthetic intelligence (AI) endows applications with the talent to automatically learn and adapt from undergo via interacting with the surroundings / environment. behold the blog “Artificial Intelligence is not Fake Intelligence” for a more minute explanation on synthetic intelligence and machine learning.

    The hasty Company article “How to cease Worrying and esteem the distinguished AI War of 2018,” projected that the AI battle would ultimately simmer down between the “AI sizable 6”:  Alphabet/Google, Amazon, Apple, Facebook, IBM, and Microsoft. However, there are other contenders worthy of consideration including GE, Tesla, Netflix, Baidu, Tencent, and Albaba.

    But what are the characteristics of organizations that will subsist the ultimate winners in this distinguished AI War? What are the behaviors and actions that will distinguish those organizations that capitalize on this AI gold rush while others “fumble the future”?

    I believe that the AI winners will absorb the following characteristics:

  • Users, not purveyors, of AI technology
  • Embrace open source for technology agility (independence)
  • Mastery of sizable Data (and no, sizable Data is not dead)
  • Let me status my case.

    #1 Users, Not Purveyors, of AI TechnologyThe Market Capitalization Leaderboard shown in motif 1 offers essential clues as to which organizations will likely subsist the AI winners. What will set these organizations apart will subsist not the selling of technology, but their talent leverage AI for “value capture.”

    Figure 1: Marketing Capitalization Leaders as of May 26, 2017.

    By the way, I mediate Kleiner Perkins was sluggish in classifying “Industry Segment.” The market leaders are less purveyors of AI technology than they are users of AI technology.

  • Less than 10% of Amazon’s revenue comes from technology (cloud); $12B in cloud revenue out of a total revenue of $136B in 2016. So what Industry Segment are they in?
  • Google had quarterly revenues (Q1, 2016) of $26B of which digital media/advertising (search) represented $23B. Their “other” businesses (including Google Cloud) were only $3B. So what Industry Segment are they in?
  • Apple’s most recent quarterly (Q3, 2016) revenues were $42B out of which the iPhone (personal communications, information and entertainment) and the associated iPhone ecosystem (iTunes, Apple Music, App Store) comprised an aggregated $37.5B.
  • Finally, I’m not aware of any AI or data technologies that Facebook sells to the generic market. Facebook generated $9.3B in revenue in Q2, 2017 of which $9.16B came from Ad revenue. So what Industry Segment are they in?
  • Mastering Value Capture. Just having the technology is not sufficient; it’s how you expend the technology to derive and then drive novel sources of customer, business, operational, and monetary value that matters. Ultimately, the AI war is about “value capture.”

    The companies listed in motif 1 are trying to dominate markets, not technology. For example:

  • Apple (#1) seeks to dominate personal communications
  • Google/Alphabet (#2) seeks to dominate digital media, advertising and personal communications
  • Amazon (#4) seeks to dominate online commerce
  • Facebook (#5) seeks to dominate convivial media, and advertising
  • Each of these AI leaders seeks to extend their value capture capabilities into novel markets, including transportation (autonomous vehicles), healthcare, finance, media, and entertainment.

    Other market leaders are too moving aggressively to exploit the power of AI to capture more customer, products and operational value. JPM Morgan (#11) is focused on pile an AI platform (see “JPMorgan Takes AI expend to the Next Level”) that will allow JPMC to dominate monetary trading. And GE (#16) has made a strategic bet with their Predix platform (see “GE’S sizable bet on Data and Analytics”) as the platform for dominating the Industrial Internet of Things.

    Microsoft (#3) is the one exception as Microsoft is a purveyor of technology. But even Microsoft is branching beyond just selling technology into trying to dominate markets such as digital media, entertainment, and convivial media where their AI “chops” can give them competitive advantages (see “The Jewel of Microsoft’s Earnings”).

    #2 Embrace Open Source for Technology Agility (Independence)AI leaders will exploit open-source traffic models to establish platform dominance/standardization, and create technology agility and independence. They will develop an enabling technology, and then give it away via open source. This enables them to encourage the growing community of developers, especially those up-and-coming developers in universities and research labs, to build out and create de facto standards around their enabling technologies.

    Open Source Leaders. The Global AI winners are significant contributors to the synthetic intelligence and machine learning open source communities. This includes developments such as Amazon Machine Learning, Google TensorFlow, Facebook Caffe2, Microsoft Azure ML Studio, Microsoft Distributed Machine Learning Toolkit, Facebook GraphQL, and Facebook Torch.

    The leadership role that the “Great AI War” combatants are playing can subsist seen in many open source projects. For example, Torch is an open source machine learning library and scientific computing framework. The “official maintainers” of Torch are:

  • Research Scientist @ Facebook
  • Senior Software Engineer @ Twitter
  • Research Scientist @ Google DeepMind
  • Research Engineer @ Facebook
  • Training and Education. Another strategy from the Global AI leaders the creation of community or industry training and education opportunities around their open source technologies. For example, Google is committing $1 billion to train American workers to build novel businesses with Google’s AI tools (see “Google Commits $1 Billion in Grants to Train U.S. Workers for High-Tech Jobs”).

    Avoiding Technology Lock-in.  But equally essential is that these AI leaders are seeking to avoid technology and architecture lock-in. They absorb watched primitive school organizations struggle with proprietary software packages that took months if not years for upgrades and bug fixes, while paying a onerous annual maintenance fees (33% of list expense means you’re buying the entire software package again every 3 years). In a world where the enabling data and analytic technologies are changing nearly daily, technological and architecture agility (at scale) and independence is mandatory for organizations looking to win the distinguished AI War.

    #3 Mastery of sizable DataEveryone knows about the astounding growth of sizable data over the eventual decade as organizations focused on capturing minute customer, product, operational and market data. Initially fueled by commerce, web and convivial media data, sizable data has accelerated with the growth of video, wearables, and the Internet of Things. (See motif 2).

    However, organizations absorb struggled to monetize this wealth of data. Enter synthetic intelligence.

    Figure 2: Fueling the Insatiable Appetite for Data

    More Data = Better AI. synthetic intelligence can exploit massive data sets to identify patterns on a scale that flummox traditional traffic Intelligence “slice and dice” and query technologies. Data is the food that feeds AI. The more data the AI models consume, the smarter AI gets. For example, Facebook is mastering facial recognition via its DeepFace deep Learning application by virtue of owning the world’s largest repository of photos.

    To illustrate the symbiotic relationship between sizable data and AI, let’s peruse at autonomous vehicles (AV). AV require gargantuan quantities of data to feed the AV machine learning algorithms. It would acquire tens of thousands of hours of real-world driving data across a variety of driving scenarios to teach cars how to navigate on their own. To address this data volume problem, AV companies are using the video game “Grand Theft Auto” to abet generate enough data in order to train Autonomous Vehicles (see “GTA is Teaching Self-Driving Cars How to Navigate Better in the true World”).

    Data Lake. Leading AI organizations are exploiting the data lake concept to not only store the growing wealth of structured and unstructured (internal and publicly-available) data, but to provide an elastic, scalable, self-provisioning data science platform for “collaborative value creation” in pile the machine learning and synthetic intelligence models (see “Data Lake traffic Model Maturity Index” for more details on data lake traffic model maturation).

    Exploiting the Economic Value of Data. Leading AI organizations realize that data and analytics are unlike any traditional corporate assets. Data and analytics are digital assets that never wear out, never deplete, and can subsist used simultaneously at near-zero marginal cost across an sempiternal traffic and operational expend cases. Understanding the righteous economic value of the organization’s data can abet to prioritize technology and traffic investments that accelerate value capture from these data sources (see University of San Francisco research paper “Determining the Economic Value of Data” for more details).

    Conclusion: How to Become an AI WinnerAs has been discussed many times in my blog series, and explored in detail in my book, “Big Data MBA: Driving traffic Strategies with Data Science,” AI winners will ultimately subsist those organizations that are the most effectual at leveraging data and analytics to power their traffic models (see motif 3).

    Figure 3: How effectual Is Your Organization at Leveraging Data and Analytics to Power Your traffic Models?

    Ultimately, AI winners will master three key characteristics:

  • Focus on Value Capture by identifying, validating and prioritizing the organization’s key traffic and operational expend cases (see “Use Case Identification, Validation and Prioritization”).
  • Avoid technology and architecture lock-in and create technology independence via an open source technology strategy
  • Mastery of sizable Data and the Data Lake to exploit the unique economic value of the data and analytic digital assets (see “Data Lake traffic Model Maturity Index”).
  • So in conclusion, let’s absorb some fun with this blog and mediate outside of the box about some hypothetical scenarios in which companies exploit this AI gold rush:

  • What would subsist the traffic model ramifications to GE if they were to open source Predix and proffer Predix training to universities and third party developers?
  • What would subsist the traffic model ramifications to JPMC if they were to open source their trading platform to universities and third party developers?
  • What would subsist the traffic model ramifications if IBM moved out of the technology purveyor traffic and instead acquired companies in monetary services and healthcare where their Watson AI platform could create market dominance?
  • As the world prepares for the impending distinguished AI war, now is not the time for organizations to subsist shy or to cling to old, outdated traffic models.

    Fortune Favors the Brave.

    Sources

    Figure 1: ScoopNest “2017 global market capitalization leader board: tech is 40% of top 20 companies and 100% of top 5” and Consultancy UK “Market capitalisation of world’s 100 biggest companies hits $17.4 trillion”

    The post 3 Keys to Winning the distinguished synthetic Intelligence (AI) War! appeared first on InFocus Blog | Dell EMC Services.

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