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C2020-013 IBM SPSS Modeler Data Mining for Business Partners v2

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C2020-013 exam Dumps Source : IBM SPSS Modeler Data Mining for Business Partners v2

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Test Name : IBM SPSS Modeler Data Mining for Business Partners v2
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IBM Watson Studio: Product Overview and perception | killexams.com Real Questions and Pass4sure dumps

See the entire listing of laptop getting to know SolutionsSee person reviews of IBM Watson Studio

base line

Watson is an umbrella for all IBM deep discovering and synthetic intelligence, in addition to computer researching. The enterprise was a pioneer in introducing AI technologies to the business world. What this means for buyers: Watson Studio is a top contender for any organization looking to set up computer studying and deep researching technologies.

The platform gives huge tools and technologies for information scientists, developers and discipline count number consultants that need to discover statistics, construct fashions, and train and deploy desktop getting to know fashions at scale. The solution includes tools to share visualizations and results with others. Watson Studio supports cloud, computing device and local deployment frameworks.

The latter resides behind a company’s firewall or as a SaaS answer operating in an IBM deepest cloud. IBM Watson Studio is ranked as a “leader” within the Forrester Wave. It changed into a valued clientele’ choice 2018 recipient at Gartner Peer Insights.

Product Description

Watson Studio relies on a set of IBM tools and applied sciences to build powerful desktop getting to know applications and capabilities. This includes IBM Cloud pretrained laptop discovering models corresponding to visual cognizance, Watson herbal Language Classifier, and others. The ambiance uses Jupyter Notebooks together with other open supply equipment and scripting languages to complement built-in collaborative challenge aspects.

https://o1.qnsr.com/log/p.gif?;n=203;c=204660772;s=9478;x=7936;f=201812281334210;u=j;z=TIMESTAMP;a=20403954;e=i

The result is an atmosphere that facilitates speedy and robust laptop discovering development and satisfactory tuning of models. statistics scientists and others can choose between quite a lot of capacities of Anaconda, Spark and GPU environments.

Watson Studio helps enhanced visible modeling via a drag-and-drop interface provided by means of IBM’s SPSS Modeler. furthermore, it accommodates automated deep discovering the usage of a drag-and-drop, no-code interface in Neural network Modeler.

Overview and lines consumer Base

records scientists, developers and discipline rely experts.

Interface

Graphical drag-and-drop and command line.

Scripting Languages/formats Supported

helps Anaconda and Apache Spark. The latter presents Scala, Python and R interfaces.

codecs Supported

Most essential facts and file codecs are supported via open supply Jupyter Notebooks.

Integration

IBM Watson Studio connects a few IBM products, together with SPSS Modeler and facts Science event (DSX) together with open supply tools, so as to convey a sturdy Predictive Analytics and desktop getting to know (PAML) solution.

The environment incorporates open data units via Jupyter Notebooks, Apache Spark and the Python Pixiedust library. The cloud edition facets interactivity with notebook servers and R Studio, along with Python, R., and Scala coder for information scientists.

Reporting and Visualization

Visualization via SPSS Modeler. effective logging and reporting capabilities are developed into the product.

Pricing

IBM has adopted a pay-as-you-go model. Watson Studio Cloud – common charges $99 monthly with 50 skill unit hours monthly blanketed. Watson Studio Cloud - commercial enterprise runs $6,000 per 30 days with 5,000 ability unit hours. Watson Studio desktop fees $199 per thirty days with limitless modeling. Watson Studio local – for enterprise information science teams N/A.

IBM Watson Studio Overview and features at a glance:

dealer and contours

IBM Watson Studio

ML focus

large records science focal point with cloud and desktop ML structures.

Key features and capabilities

amazing visual cognizance and natural classification tools. flexible method that incorporates open supply tools. Connects to IBM SPSS Modeler.

person comments

totally rated for aspects and capabilities. Some complaints revolving around using notebooks.

Pricing and licensing

Tiered model from $99 per month per person to $6,000 per month per user or more at business stage.


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

synthetic Intelligence in IT today

Many IT departments have carried out application solutions that go past fundamental transaction and analytical processing. These programs contain models that describe definite records behaviors, and these fashions consume latest statistics to look if these patterns of data conduct exist. in that case, operational methods can use this tips to make choices. a good example of here is fraud detection. IT records engineers use analytics on ancient information to examine when fraud occurred, code this into a model, and set up the mannequin as a service. Then, any operational gadget can invoke the mannequin, move it present facts and obtain a mannequin “rating” that represents the chance that a transaction can be fraudulent.

The generic time period for these new packages is artificial intelligence (AI). They include a combination of search, optimization and analytics algorithms, statistical analysis concepts and template procedures for ingesting records, executing these options and making the results purchasable as functions called models. The subset of AI that deals with mannequin advent and implementation is sometimes called desktop researching (ML).

laptop getting to know and artificial Intelligence

IT departments implement ML and AI solutions within the broader context of their statistics and processing footprint. here is always depicted because the following 4-layer hierarchy.

Layer 1: The facts.

this residue carries the statistics dispensed across the commercial enterprise. It contains mainframe and allotted records such as product and sales databases, transactional statistics and analytical data in the statistics warehouse and any huge data applications. It also may also include consumer, seller and company records, perhaps at remote websites, and even extends to public information comparable to twitter, news feeds and survey results. a different feasible supply of facts is server efficiency logs that encompass aid utilization heritage.

notice that these information exist across diverse hardware structures including on-premises and cloud-primarily based. As such, a number of information aspects can exist in distinctive forms and codecs (e.g. text, ASCII, EBCDIC, UTF-8, XML, pictures, audio clips, and many others.). additionally, at this stage will exist hardware and utility that control the data, including high-velocity statistics loaders, information purge and archive approaches, publish-and-subscribe approaches for facts replication, in addition to these for commonplace backup and recovery and disaster healing planning.

Layer 2: The Analytics Engines.

in this layer exist a mixture of hardware and application that executes business analytics in opposition t the records layer. There are a few average avid gamers in this area. They encompass:

  • The IBM Db2 Analytics Accelerator (IDAA) than may also be implemented as standalone hardware or totally built-in within certain z14 servers;
  • Spark on z/OS;
  • Spark Anaconda on z/OS;
  • Spark clusters on allotted systems.
  • just as the statistics layer happens across numerous hardware platforms and disbursed sites, so will the analytics engines layer. The major function of this accretion is to deliver an optimized statistics access layer in opposition t the underlying data as a service for AI and operational purposes.

    Layer three: The laptop gaining knowledge of Platform.

    IT implements desktop researching utility during this layer. It accesses the information via one or more of the analytics engines. it is during this layer that IBM offers its newest offering, Watson computer learning for z/OS (WMLz). WMLz provides a fundamental desktop researching workflow including right here steps:

  • statistics Ingestion and coaching — Inputting records, filling in lacking values, encoding category information, growing indexes and normalizing numeric values;
  • model building and practising — An interface for the data scientist to create a model of records conduct in line with old analytics, coach the model to become aware of statistics patterns and validate the model;
  • mannequin Deployment — put into effect the mannequin as a creation technique, together with methods for updating models in-location and monitoring mannequin effects;
  • feedback Loops — techniques that enable automated mannequin learning via feeding mannequin outcomes back into the mannequin practising procedure to update models or produce new ones.
  • records scientists know that one of the crucial most effective benefits of laptop learning is to use the effects in operational techniques; as an example, having an ML model analyze monetary information to check the probability of fraud. This ability that you will achieve foremost performance if you set up ML within the hardware ambiance the place transaction processing occurs. for many enormous organizations this skill the IBM zServer atmosphere.

    Layer four: laptop gaining knowledge of solutions.

    Now that we've the computing device studying platform available as ML functions, they are able to create mixed AI/ML options that invoke these functions. IBM has a few competent-made options for this deposit, together with the following:

  • Db2 AI for z/OS (Db2ZAI) -- the use of Db2 SMF data for evaluation, Db2ZAI monitors and analyzes Db2 operations in a Z/OS environment. it may supply superior query access route assistance to the Db2 optimizer to raise SQL performance, diagnose Db2 efficiency abnormalities and suggest corrective action and observe Db2 information anomalies and provide performance tuning concepts;
  • IBM Z Operations Analytics (IZOA) -- This product analyzes z/OS SMF information and detects adjustments in subsystem use and forecasts alterations that may be required in the future, does automated problem evaluation and provides difficulty insights from general issue signatures.
  • Watson laptop learning on Z

    Let’s take a deeper dive into how Watson computing device discovering on Z (WMLz) works and what services it can deliver.

    Key performance warning signs (KPIs). WMLz doesn't inherently understand what efficiency factors are important to you. despite the fact, as soon as these KPIs are described (either by using a person or by imposing one of the crucial desktop gaining knowledge of options referred to above), WMLz can analyze KPI information to search for correlations. for example, when one KPI (say, I/O against a crucial database) goes up, one other KPI (say CPU utilization) may additionally go up as well. As an extra example, a few KPIs may be behaviorally identical, so WMLz can cluster them as a bunch and perform additional evaluation across corporations. WMLz can additionally verify KPI baseline behaviors in line with time-of-day, time zone of transactions or seasonal pastime.

    Anomaly Detection. once correlations are discovered, WMLz can appear opposite effects and document them as anomalies. In their I/O instance above, an anomaly would be reported if I/O towards a important database expanded but CPU utilization lowered.

    sample attention. As with many desktop learning engines, WMLz will look for patterns among KPIs and statistics identifiers. as an instance, CPU may additionally raise when processing definite classes of transactions.

    KPI prediction. An extension of fundamental KPI processing, WMLz can use the previous behaviors of agencies of KPIs to foretell the future. consider their I/O illustration as soon as once more. The product might also realize that certain transactions develop into extra numerous throughout a selected time period, and these transactions eat vastly greater CPU cycles. The product may additionally then predict future CPU spikes.

    Batch workload analysis. Many IT stores have a big contingent of batch processing that is tightly scheduled and comprises job and aid dependencies. Some jobs ought to wait for his or her predecessors to comprehensive, some use enormous shared elements (similar to tape drives or area of expertise hardware) and some are so aid-intensive that then can not be done on the same time. WMLz can analyze the workload records, including useful resource usage, and supply techniques for balancing resources or tuning elapsed instances.

    MLC can charge pattern evaluation and price discount. Some IBM application license charges are billed monthly, and the license volume may additionally depend upon highest CPU utilization all over top intervals. WMLz can analyze CPU usage throughout time, look for patterns and make predictions and proposals for application license charge discount.

    Watson computer researching for z/OS — elements

    IBM’s Watson desktop gaining knowledge of for z/OS allows for IT its option of development environments to boost fashions together with IBM SPSS Modeler. These environments aid statistics scientists by using notebooks, information visualization equipment and wizards to velocity the construction method. a number of brief-birth software templates are additionally integrated in the toolset for usual enterprise requirements equivalent to fraud detection, load approval and IT operational analytics. The newest version of WMLz (edition 2.1.0) comprises help for Ubuntu Linux on Z, java APIs, simplified Python kit administration and a couple of different facets.

    interested readers may still reference the links under for more detailed technical suggestions.

    # # #

    See all articles by Lockwood Lyon

    REFERENCES

    desktop researching and artificial Intelligencehttps://en.wikipedia.org/wiki/Machine_learning

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

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

    Watson laptop studying - Overviewhttps://www.ibm.com/cloud/laptop-gaining knowledge of

    Watson desktop gaining knowledge of - Resourceshttps://www.ibm.com/cloud/desktop-gaining knowledge of/resources


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    IBM SPSS Modeler Data Mining for Business Partners v2

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    DePaul University and IBM Team to Launch New Center for Data Mining and Predictive Analytics | killexams.com real questions and Pass4sure dumps

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  • Aginity Wins IBM Business Partner Leadership Award | killexams.com real questions and Pass4sure dumps

    LAS VEGAS and EVANSTON, Ill., Feb. 16, 2017 /PRNewswire/ -- Yesterday, IBM presented Aginity with a 2017 IBM North America Top Independent Software Vendor Leadership Award. The award acknowledges the extensive collaboration between Aginity and IBM in 2016, with more than 20 successful joint deployments in North America and worldwide across industries such as retail, consumer products, media and financial services.

    Aginity Amp™ analytics management software, along with IBM Analytics and Watson Customer Engagement solutions, helps companies measurably improve business outcomes. Amp works seamlessly with IBM products including SPSS [® ] and IBM Modeler™ for statistics and data mining, IBM Cognos [®] for reporting, IBM Tivoli [®] for scheduling and IBM Campaign for marketing automation. Aginity software been deployed dozens of times alongside the IBM PureData [®] for Analytics Solution, powered by Netezza technologies -- often in collaborative engagements with IBM Global Business Services.

    "We are proud to be recognized for the unique value that Aginity delivers as an IBM Business Partner from the start of the sales cycle through to full deployment of joint solutions," said Mike Blyth, executive vice president for strategic development at Aginity. "Amp helps companies overcome challenges such as siloed data or a shortage of skilled data scientists by enabling them to readily combine data from various sources and make analytic 'building blocks' accessible across users, applications and geographies."

    Companies can catalog analytics in Amp, which enables analysts to reuse them in a variety of applications and to build additional analytics. In one engagement, for example, Amp enabled a global company to integrate 10 years of customer data for seven brands across three geographic regions in 90 days. Efficiencies enabled by analytic reuse enabled the company to increase the number of weekly marketing offers delivered through IBM Campaign from seven to 35 while also improving the targeting of those offers.

    Neal Callahan, vice president of IBM Global Business Partners for North America, presented the award at the IBM PartnerWorld Leadership Conference in Las Vegas, which this year attracted more than 2,500 attendees. A Gold Level IBM Business Partner, Aginity has twice won prestigious IBM Beacon Awards – in 2013 in the Retail Solutions category and in 2015 for Big Data and Analytics Solutions. In 2016, Aginity began offering Amp globally through the IBM Marketplace.

    About Aginity

    Founded in 2005, Aginity transforms the way companies compete on analytics. Aginity Amp™ software enables companies to create, catalog and manage analytics as assets which can then be reused consistently everywhere in the enterprise. Customers have seen significant operational cost savings and achieved improvements in business outcomes by applying the governed, reusable analytics that Amp enables. Aginity is a privately held company headquartered in the Chicago area. For more information, visit www.aginity.com.

    For more information: Kelly McClure, kmcclure@aginty.com

    To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/aginity-wins-ibm-business-partner-leadership-award-300408895.html

    SOURCE Aginity

    Copyright (C) 2017 PR Newswire. All rights reserved


    IBM, USC Tackle Major League Data | killexams.com real questions and Pass4sure dumps

    Researchers at the University of Southern California’s Annenberg Innovation Lab released a joint statement with IBM today that reveals how academia is making use of big social data.

    The Annenberg Innovation Lab has created a “Social Sentiment Index” using IBM Social Analytics technology to sift through innumerable tweets about everything from retail to fashion. The social media analytics project involved sentiment around the National League Championship Series, and will, according to a statement from IBM “broaden the index to follow the World Series games beginning tomorrow to determine ‘social media MVPs.’

    Last May IBM released its social media analytics tool, which was designed to capture data from a number of social networks, including Twitter. These analytics tools, buckled under the name SPSS Modeler Data Mining and Text Analytics Workbench, uses natural language process to take the temperature of everything from brand names to slang and overall “mood” of the Twitter kingdom.

    IBM claims a number of customers for this product already, including Rosetta Stone and Money Mailer.  While some argue that price and learning curve for the product are far beyond the grasp of SMBs, “for larger enterprises that need robust technologies and can’t risk entrusting data collection to a startup web app, IBM’s software might very well provide the features they need.”

    The researchers hope to show how it is possible to discover previously unseen trends and insights from the social media data to demonstrate how data-driven approaches can be valuable in other contexts.

    The USC Annenberg Social Sentiment Index has already been used to harness social media data to predict film box office success and to identify top retail fashion trends.

    According to Jonathan Taplin, USC professor and director of the Annenberg Innovation Lab, “Our work with IBM is a great demonstration of how Watson-inspired technologies, like sophisticated semantic and linguistic analysis software, can crunch Big Data to quickly gain temperature checks on timely issues.”

    Taplin points to the competitive advantage an analytics background can provide students, noting that “Throughout the past year, their students have developed analyses across other industries including movies and fashion retail. By taking a deep dive into the Twitterverse — concentrating on the short 140 character tweets and polling large data sets over a number of days – students are able to determine positive or negative sentiments in a matter of minutes. The results are helping students gain highly sought skills in analytics that can set them apart in the business world.”

    IBM’s partnership with USC’s Annenberg Innovation Lab is part of its push to increase analytics skills in academia. Big Blue says it’s working with 6,000 universities worldwide “to develop curricula and provide training, resources and support for business analytics.”

    Curious about the findings of the analytics exercise? Read more here…



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