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IBMSPSSMBPDA IBM SPSS Modeler - Business(R) Partner Data Analyst Associate

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IBMSPSSMBPDA exam Dumps Source : IBM SPSS Modeler - Business(R) Partner Data Analyst Associate

Test Code : IBMSPSSMBPDA
Test Name : IBM SPSS Modeler - Business(R) Partner Data Analyst Associate
Vendor Name : IBM
: 25 Real Questions

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IBM IBM SPSS Modeler -

Open supply and IBM SPSS Modeler

A successful information-pushed organization has to supply the right tools for its information analysts, builders, and enterprise end users. more and more, this ability leveraging open-supply application. The R or Python programming languages, together with the Apache Spark and Hadoop information frameworks, are favorite open-supply equipment for many modern analysts and builders. however for all their merits, widespread open-supply facts courses also include potentially time-drinking drawbacks.


IBM Analyzes Social network Sentiment With SPSS Modeler upgrade

IBM_logo_2009.jpg IBM (news, site) has just launched an upgrade to the SPSS predictive analytics tool as a way to enable users to research text gathered from social media sources, including Twitter, facebook, blogs, wikis and RSS feeds.

The upgrade represents a considerable improve in the SPSS Modeler statistics mining and text analytics solution with potential that stretch so far as detecting the context of the phrases used. information from the social net can even be merged with internal records to create much more correct intelligence about patrons, IBM says.

With its new capacity to consider emoticons, slang and other social media terminology, as well as jargon from specific industries, it comprises taxonomies from 180 verticals ranging from existence Sciences to fiscal, assurance and consumer Electronics. IBM SPSS Modeler 14 top rate edition is designed to investigate any suggestions that gives businesses insights into how their items are being bought.

IBM And SPSS

this is the primary main upgrade to Modeler because IBM purchased out predictive analytics company SPSS for US$ 1.2 billion closing October.

on the time IBM stated the acquisition would additional expand its suggestions on Demand (IOD) utility portfolio and business analytics capabilities. Forrester is deciphering the stream as the first salvo in a combat for supremacy in the enterprise intelligence market.

SPSS Modeler 14

And with this upgrade, IBM has introduced to its social and enterprise analytics capabilities and definitely upped the ante for competitors. Modeler 14 premium edition, by way of analyzing sentiment in social media, can predict what patrons are interested in, where they'll go, and what practices will retain them involved.

SPSS_workbench.jpgIBM SPSS Modeler 14 top rate edition workbench 

It comes with new semantic networks and greater than four hundred,000 phrases across most verticals in addition to one hundred,000 synonyms and hundreds of manufacturers enabling users to hyperlink sentiment and products while not having to build up their personal definitions inside these 180 taxonomies.

SPSS_Add text.jpg

in this appreciate, IBM cites the banking industry for example the place the semantic community overlaying it will probably distinguish between plenty of the terminology governing loan lending. It knows, for instance, that a “floating rate” is a similar as a loan mortgage and that variable and adjustable loan quotes are conveniently the identical issue.

business clever selections

The base line here is stronger enterprise decision making. the use of natural Language Processing it takes key concepts, opinions and expressed sentiment from the web, together with survey information and text and integrates that statistics into fashions designed to predict consumer reaction as well as recommending superior business selections.

by means of placing this together with the structured data that already exists in the enterprise, clients can discover relationships between concepts and purchaser movements with a purpose to construct better understanding of client businesses and the way they are reacting in a selected channel.

IBM BI expansion

And as to why IBM went down this route in the first place, at the time of the SPSS acquisition Forrester (information, site) referred to three reasons:

  • continued increase of its counsel On Demand pressure -- its smallest enterprise enviornment -- that has considered it buy out Ascential software, FileNet and Cognos. IBM’s imaginative and prescient of a wiser Planet combines instrumentation, interconnectivity and intelligence. without SPSS, Forrester says, IBM would not be capable of carry on this.
  • Strategic flow to counteract the acquisition of sun Microsystems by means of Oracle and put itself forward of the different huge guns at a functions degree.
  • Improves market placement by way of placing itself in a higher position than some other supplier to deliver integrated smart solutions sooner or later, from physical world awareness to choice guide equipment.
  • The newest version of IBM SPSS Modeler statistics mining and textual content analytics workbench is now accessible global. textual content analytics workbench is simply attainable in IBM SPSS Modeler top rate edition. 


    eQHealth options companions with IBM to increase visual records Science Capabilities

    eQHealth options, a pacesetter in inhabitants health administration and clinical administration solutions, lately became an IBM business accomplice and utilizer of the IBM Embedded Analytics solution. The aim of the settlement is to beef up eQHealth’s analytics capabilities in digesting and extracting that means and value from structured and unstructured text healthcare records from care coordinator’s health possibility assessments, clinician notes, and different types of unstructured records. The IBM data Science Platform lets eQHealth leverage SPSS Modeler for predictive modeling, computing device studying, statistical evaluation, textual content analytics and resolution optimization. this will enable eQHealth to hastily progress from information exploration to perception and creation. The IBM SPSS Modeler also offers convenient deployment for laptop researching. further, SPSS Modeler will boost latest predictive modeling and possibility stratification capabilities.

    eQHealth has employed information analytics and records to supply insights and choice guide for medical advice and scientific administration process advancements for their purchasers and developed the first edition of the business’s proprietary Peer Utilization evaluation application (PURs) greater than 25 years ago. adding IBM SPSS Modeler is anticipated to provide an further layer of functionality to existing competences.

    “IBM SPSS Modeler is distinct on account of its potential to guide many statistics types and sources, supply a range of algorithmic strategies and computer discovering strategies, all without the want for complex coding easing the assignment of integration and velocity to answer deployment. They seem to be forward to a long and productive relationship with IBM and the SPSS Modeler team,” referred to Mayur Yermaneni, Chief approach and growth Officer, eQHealth options.

    About eQHealth solutions

    based in 1986, eQHealth options is a inhabitants health administration and expertise solutions business that touches tens of millions of lives yearly during the nation. Their excessive-tech and high-touch fashions include creative expertise solutions, fine care coordination functions, and center of attention on first rate effects and optimization of issuer and payer networks. eQHealth serves a lot of entities together with federal, state and business customers. www.eqhs.org

    View source version on businesswire.com: https://www.businesswire.com/news/home/20190117005037/en/

    source: eQHealth options"> <Property FormalName="PrimaryTwitterHandle" value="@eqhealth

    eQHealth SolutionsKatie Varnado, 225-248-7069Channel advertising Managereqhealth@eqhs.org

    Copyright enterprise Wire 2019


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    A Pure Play On Self-Service Big Data Prep And Analytics: Wait For Smarter Valuation Entry Point

    No result found, try new keyword!Big data warehouses, data lakes keep growing exponentially and descriptive, diagnostic, predictive and prescriptive analytics allow data scientists, citizen data scientists and guided business analyst ...

    Your guide to the top big data certifications today

    IDG

    IDG

    Data and big data analytics are fast becoming the lifeblood of any successful business. Getting the technology right can be challenging, but building the right team with the right skills to undertake big data initiatives can be even harder.

    Not surprisingly, that challenge is reflected in the rising demand for big data skills and certifications. According to research by IT research firm Foote Partners, both noncertified advanced data analytics skills and certified big data skills have gained value in recent years: with 74 advanced data analytics related skills and certifications rising in average value by 6 percent in 2015, followed by 116 advanced data analytics related skills and certifications increasing 4.8 percent overall in market value in 2016. Additionally, Foote Partners research found 123 related certified and noncertified big data skills seeing a 0.3 percent gain in value in the first quarter of 2017.

    Organizations are on the hunt for data scientists and analysts with expertise in the techniques required to analyze big data. They also need big data systems architects to translate requirements into systems, data engineers to build data pipelines, developers who know their way around Hadoop clusters and other technologies, and systems administrators and managers to tie everything together.

    [ Find out the hottest data and analytics trends today. | Get ahead with the top certs for big data, project management, agile, and the cloud. | Get weekly career tips by signing up for their CIO Leader newsletter. ]

    These skills are in high demand and are relatively rare. Individuals with the right mix of experience and skills can demand high salaries. The right certifications can help.

    "Advanced data analytics capabilities are just too critical for staying competitive," David Foote, co-founder, chief analyst and chief research officer of Foote Partners, said in a statement released with the research. "They've expanded in popularity from a few industries to nearly every industry and market. And there is the Internet of Things, the next critical focus for data and analytics services. IDC is predicting a 30 percent CAGR over the next five years, while McKinsey is expecting IoT to have a $4 trillion to $11 trillion global economic impact by 2025 as businesses look to IoT technologies to provide more insight."

    While the market value of noncertified advanced analytics skills has actually increased faster as a percentage of base salary than the value of certified big data skills, according to Foote Research, Foote believes pay premiums for both noncertified and certified skills will steadily rise over the next 12 to 24 months.

    If you're looking for a way to get an edge — whether you're job hunting, angling for a promotion or just want tangible, third-party proof of your skills — big data certification is a great option. Certifications measure your knowledge and skills against industry- and vendor-specific benchmarks to prove to employers that you have the right skillset. The number of big data certs is expanding rapidly.

    Below is their guide to the most sought after big data certifications to help you decide which cert is right for you.

    If you would like to submit a big data certification to this directory, please email us.

    Analytics: Optimizing Big Data Certificate

    The Analytics: Optimizing Big Data Certificate is an undergraduate-level program intended for business, marketing and operations managers, data analyst and professionals, financial industry professionals, and small business owners. The program brings together statistics, analysis, and written and oral communications skills. It introduces students to the tools needed to analyze large datasets, covering topics including importing data into an analytics software package, exploratory graphical and data analysis, building analytics models, finding the best model to explore correlation among variables and more.

    Organization: University of Delaware

    Price: $2,895 course fee

    How to prepare: A basic background in statistics and some prior college coursework is recommended.

    Certificate in Engineering Excellence Big Data Analytics and Optimization (CPEE)

    Offered in Hyderabad and Bengaluru, India, the Certificate in Engineering Excellence Big Data Analytics and Optimization is an intensive 18-week program that consists of 10 courses (lectures and labs) for students of all aspects of analytics, including working with big data using Hadoop. It focuses on R and Hadoop skills, as well as statistical modeling, data analytics, machine learning, text mining and optimization. Students are evaluated on a real-world capstone project and a series of quizzes.

    Organization: International School of Engineering (INSOFE)

    Price: ₹3000 (INR) application fee and a program fee of ₹3,25,000 + 15 percent service tax.

    How to prepare: INSOFE admits students based on performance on its entrance exam and prior academic background and work experience.

    Certification of Professional Achievement in Data Sciences

    The Certification of Professional Achievement in Data Sciences is a non-degree program intended to develop facility with foundational data science skills. The program consists of four courses: Algorithms for Data Science (CS/IEOR), Probability & Statistics (STATS), Machine Learning for Data Science (CS), and Exploratory Data Analysis and Visualization (STATS).

    Organization: Columbia University

    Price: $1,858 per credit (a minimum of 12 credits, including the four courses, are required to complete the program). In addition, there is an $85 non-refundable application fee for the on-campus program and $150 for the online program. The online program also includes an additional non-refundable technology fee of $395 per course.

    How to prepare: An undergraduate degree and prior quantitative and introductory to computer programming coursework are required.

    Certified Analytics Professional

    The Certified Analytics Professional (CAP) credential is a general analytics certification that certifies end-to-end understanding of the analytics process, from framing business and analytic problems to acquiring data, methodology, model building, deployment and model lifecycle management. It requires completion of the CAP exam and adherence to the CAP Code of Ethics.

    Organization: INFORMS

    Price: $495 if you are an INFORMS member, or $695 if you're not. Team pricing is available for organizations. 

    How to prepare: A list of study courses and a series of webinars are available through registration.

    Cloudera Certified Associate (CCA) Data Analyst

    A SQL developer who earns the CCA Data Analyst certification demonstrates core analyst skills to load, transform and model Hadoop data to define relationships and extract meaningful results from the raw output. It requires passing the CCA Data Analyst Exam (CCA159), a remote-proctored set of eight to 12 performance-based, hands-on tasks on a CDH 5 cluster. Candidates have 120 minutes to implement a technical solution for each task. They must analyze the problem and arrive at an optimal approach in the time allowed.

    Organization: Cloudera

    Price: $295

    How to prepare: Cloudera recommends candidates take the Cloudera Data Analyst Training course, which has the same objectives as the exam.

    Cloudera Certified Associate (CCA) Spark and Hadoop Developer

    The CCA Spark and Hadoop Developer credential certifies a professional has proven their core skills to ingest, transform and process data using Apache Spark and core Cloudera enterprise tools. It requires passing the remote-proctored CCA Spark and Hadoop Developer Exam (CCA175), which consists of eight to 12 performance-based, hands-on tasks on a Cloudera Enterprise cluster. Each question requires the candidate to solve a particular scenario. Some cases may require a tool such as Impala or Hive, others may require coding. Candidates have 120 minutes to complete the exam.

    Organization: Cloudera

    Price: $295

    How to prepare: There are no prerequisites required, but Cloudera says the exam follows the same objectives as the Cloudera Developer Training for Spark and Hadoop course, making it excellent preparation for the exam.

    Cloudera Certified Professional (CCP): Data Engineer

    The CCP: Data Engineer credential certifies the ability to perform core competencies required to ingest, transform, store and analyze data in Cloudera's CDH environment. It requires passing the remote-proctored CCP: Data Engineer Exam (DE575), a hands-on, practical exam in which each user is given five to eight customer problems each with a unique, large data set, a CDH cluster and four hours. For each problem, the candidate must implement a technical solution with a high degree of precision that meets all the requirements.

    Organization: Cloudera

    Price: $400

    How to prepare: Cloudera suggests professionals seeking this certification have hands-on experience in the field and take the Cloudera Developer Training for Spark and Hadoop course.

    EMC Proven Professional Data Scientist Associate (EMCDSA)

    The EMCDSA certification demonstrates an individual's ability to participate and contribute as a data science team member on big data projects. It includes deploying the data analytics lifecycle, reframing a business challenge as an analytics challenge, applying analytic techniques and tools to analyze big data and create statistical models, selecting the appropriate data visualizations and more.

    Organization: Dell EMC Education Services

    Price: $600 for video-ILT streaming; $5,000 for instructor-led

    How to prepare: EMC offers a training course, available as either a video or an instructor-led course.

    IBM Certified Data Architect – Big Data

    Designed for data architects, the IBM Certified Data Architect – Big Data certification requires passing a test that consists of five sections containing a total of 55 multiple-choice questions. It demonstrates a data architect can work closely with customers and solutions architects to translate customers' business requirements into a big data solution.

    Organization: IBM Professional Certification Program

    Price: $200

    How to prepare: IBM recommends a series of seven multi-day courses on SPSS Modeler to InfoSphere BigInsights to prepare for the test.

    IBM Certified Data Engineer – Big Data

    The IBM Certified Data Engineer – Big Data certification is intended for big data engineers, who work directly with data architects and hands-on developers to convert an architect's big data vision into reality. Data engineers understand how to apply technologies to solve big data problems and have the ability to build large-scale data processing systems for the enterprise. They develop, maintain, test and evaluate big data solutions within organizations, providing architects with input on needed hardware and software. This certification requires passing a test that consists of five sections containing a total of 53 multiple-choice questions.

    Organization: IBM Professional Certification Program

    Price: $200

    How to prepare: IBM recommends a series of nine multi-day courses to prepare for the test.

    Mining Massive Data Sets Graduate Certificate

    Designed for software engineers, statisticians, predictive modelers, market researchers, analytics professionals, and data miners, the Mining Massive Data Sets Graduate Certificate requires four courses and demonstrates mastery of efficient, powerful techniques and algorithms for extracting information from large datasets like the Web, social network graphs and large document repositories. The certificate usually takes one to two years to complete.

    Organization: Stanford Center for Professional Development

    Price: $18,000 tuition

    How to prepare: A Bachelor's degree with an undergraduate GPA of 3.0 or better is required. Applicants should have knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program.

    MongoDB Certified DBA Associate

    The MongoDB Certified DBA Associate credential is intended to demonstrate that operations professionals understand the concepts and mechanics required to administrate MongoDB. It requires a 90 minute, multiple choice exam.

    Organization: MongoDB University

    Price: $150

    How to prepare: There are no prerequisites, but MongoDB suggests candidates complete an in-person training or one of its online courses (M102: MongoDB for DBAs; M202: MongoDB Advanced Deployment Operations). MongoDB also provides the MongoDB Certification Exam Study Guide, available to those who have registered for a certification exam.

    MongoDB Certified Developer Associate

    The MongoDB Certified Developer Associate credential is intended for software engineers who want to demonstrate a solid understanding of the fundamentals of designing and building applications using MongoDB. It requires a 90 minute, multiple choice exam.

    Organization: MongoDB University

    Price: $150

    How to prepare: There are no prerequisites, but MongoDB suggests candidates complete an in-person training or one of its online courses (M101J: MongoDB for Java Developers; M101JS: MongoDB for Node.js Developers; M101N: MongoDB for .NET Developers; M101P: MongoDB for Developers). MongoDB also provides the MongoDB Certification Exam Study Guide, available to those who have registered for a certification exam.

    SAS Certified Big Data Professional

    The SAS Certified Big Data Professional certification program is for individuals seeking to build on their basic programming knowledge by learning how to gather and analyze big data in SAS. The program focuses on SAS programming skills; accessing, transforming and manipulating data; improving data quality for reporting and analytics; fundamentals of statistics and analytics; working with Hadoop, Hive, Pig and SAS; and exploring and visualizing data. The program includes two certification exams, both of which the participants must pass.

    Organization: SAS Academy for Data Science

    Price: $9,000 for classroom (Cary, NC), $4,725 for blended learning (combination of 24/7 online access and instructor-led training)

    How to prepare: At least six months of programming experience in SAS or another programming language is required to enroll.

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    Top Machine Learning Solutions

    In today's hyper-fast cloud computing era, machine learning solutions drive exponential progress in improving systems. Machine learning's ability to leverage Big Data analytics and identify patterns offers critical competitive advantage to today's businesses.

    Often used in combination with artificial intelligence and deep learning, machine learning uses sophisticated statistical modeling. These complex systems may reside in private cloud or public cloud. In any case, the passage of time boosts machine learning: as more data is added to a task and analyzed over time, ML produces more accurate the results.

    The global machine learning market totaled $1.4 billion in 2017, according to BCC Research. It is estimated to top $8.8 billion by 2022, a jaw-dropping compound annual growth rate (CAGR) of 43.6 percent.

    When considering which machine learning vendor to select, weigh these factors:

  • Major platform or stand alone vendor: some of the ML vendors below offer a full cloud computing platform, others are stand alone firms dedicated to ML. There's no hard and fast rule, but the stand alone firms may be more incentivized to bid competitively for your business.
  • Type of machine learning: Do you want to implement ML for retail? For healthcare? Ask your potential vendor what sectors they have invested in the most heavily.
  • Vendor's future direction: Where directions, in terms of R&D and investment, will this vendor focus on in the years ahead? That answer to that question might help you find the best match for your long term goals.
  • https://o1.qnsr.com/log/p.gif?;n=203;c=204660771;s=9478;x=7936;f=201812281332180;u=j;z=TIMESTAMP;a=20403946;e=i

    The machine learning landscape is changing rapidly. Machine learning start-ups are constantly jumping into the space. Established vendors are introducing a variety of offerings that use machine learning in one form or another. Sorting through the choices and options can prove confusing. We’ve identified eight top vendors in the machine learning space, based on the features they offer, analyst reviews, customer testimonials and independent research.

    Jump to:

    Alteryx

    Alteryx offers a unified platform that addresses data, analytics and machine learning. The vendor strives to minimize the need for data scientists by creating new and improved partnerships among IT, analytics experts and lines of business through a collaborative approach.

    The product is available in both a code-free and code-friendly format. It uses a drag-and-drop workflow interface and ties into third party demographic, firmographic and spatial data. The company is rated as a “Leader” on the Gartner Magic Quadrant (MQ). Alteryx is moving toward powerful automation and rule-based recommendations, Gartner noted.

    Alteryx offers integration with numerous major partners, including Tableau, AWS, Teradata, Microsoft, DataRobot, Salesforce, Oracle, Cloudera and Qlik. Machine learning functions feature parallel model analysis with predictive analytics, along with the ability to automate workflows and various processes.

    Read their in-depth review of AlteryxSee user reviews of Alteryx

    AWS SageMaker

    The fully managed machine learning service allows data scientists and developers to build and train machine learning models and insert them into applications. This makes it possible to run parallel models, conduct flexible distributed training, and tackle A-B testing along with other tasks.

    SageMaker is a fully managed AWS service that includes an integrated Jupyter authoring notebook instance. This aids in exploration and analysis by offering deep visibility into data sources. SageMaker offers a set of algorithms optimized for the environment and it supports proprietary algorithms and custom training scripts.

    Sagemaker also features integration with Docker containers and Apache Spark. Together with AWS, it’s possible to use the integrated SageMaker console to launch and scale projects quickly. The platform supports HIPAA and other compliance standards.

    Read their in-depth review of SageMakerSee user reviews of SageMaker

    Google Machine Learning Engine

    Google Cloud Machine Learning (ML) Engine is a fully managed platform designed for developers and data scientists. The product allows users to construct high quality models using multiple machine learning frameworks, including scikit-learn, XGBoost, Keras, and TensorFlow.

    The latter is a sophisticated deep learning framework that powers many Google products, from Google Photos to Google Cloud Speech. It allows users to deploy machine learning into production without the need for Docker containers. The highly scalable environment—which accommodates training on managed clusters, delivers a framework for automatically designing and evaluating model architectures. It is designed to accommodate existing workflows, and it scales to handle batch prediction and online prediction requirements.

    Other key features include: automatic resource provisioning, the use of portable models, server-side processing and integrated cloud data storage.

    Read their in-depth review of Google Machine Learning EngineSee user reviews of Google Machine Learning Engine

    IBM Watson Studio

    IBM was a pioneer in the artificial intelligence (AI) and machine learning spaces. Today, Forrester ranks the company as a “Leader” among multimodal predictive analytics and machine learning solutions providers.

    IBM Watson Studio is designed to accommodate a variety of independent platforms and various types of power users. This includes data engineers, app developers and data scientists. The result is strong collaboration capabilities. Among its top features: a robust architecture, strong algorithms and a powerful ability to execute machine learning.

    The Watson Studio is part of an extensive collection of technologies and solutions that are available under the IBM Watson umbrella. It is designed to accommodate IBM’s SPSS modeling workflows but also plug in open source machine learning libraries and notebook-based interfaces. The solution is available in the cloud on or on the desktop. It incorporates visual drag-and-drop tools.

    Read their in-depth review of IBM Watson StudioSee user reviews of IBM Watson Studio

    Microsoft Azure Machine Learning Studio

    Azure Machine Learning Studio has emerged as a leading solution in the managed cloud space. It delivers a visual tool that aids developers, data scientists and non-data scientists in designing machine learning pipelines and solutions that address a wide variety of tasks. The platform offers a browser-based, visual drag-and-drop authoring environment that requires no coding. Gartner ranks Microsoft a “Visionary” in its MQ. The solution offers a high level of flexibly, extensibility and openness.

    Azure ML Studio also delivers comprehensive capabilities across the full range of descriptive, diagnostic, predictive and prescriptive analytic types. Microsoft is continuing to expand the features and capabilities within Azure Machine Learning. This includes promoting deep learning through the Microsoft Cognitive Toolkit (formerly CNTK) as well as the joint ONNX open standard for neural networks. Microsoft also plans to automate a growing number of functions within the Azure Machine Learning platform.

    Read their in-depth review of Microsoft Azure Machine Learning StudioSee user reviews of Microsoft Azure Machine Learning Studio

    RapidMiner

    RapidMiner offers data prep, machine learning and model deployment tools that deliver deep insight into data. It delivers a platform that is designed to tackle a variety of big data and analytics requirements.

    In the machine learning space, the company has established itself as a heavyweight. It delivers a simple-to-use visual environment that incorporates automated model creation along with suggestions, guides and information about what approach might work best for a given situation. RapidMiner’s features and high level of flexibility have established it as a popular choice among machine learning solutions.

    Data scientists and non-data scientists alike can easily inspect and customize projects and build on results. RapidMiner is ranked as a “Leader” in both the Gartner MQ and the Forrester Wave. It is among the highest rated data and machine learning solutions at Gartner Peer Insights.

    Read their in-depth review of RapidMinerSee user reviews of RapidMiner

    SAP Leonardo

    Leonardo serves as an umbrella for a variety of SAP data products and services. It incorporates AI and machine learning through a cloud platform. This includes a machine learning foundation that allows developers, data scientists and others to create, run and deploy algorithms and machine learning apps.

    The technology extracts data from text, images, natural language and other media to generate computer-based predictions. The highly scalable platform includes predefined and pretrained machine learning services, but it also incorporates TensorFlow models. This allows users to adopt a flexible framework to training and tuning machine learning. Forrester ranks SAP as a “strong performer” in the overall predictive analytics and machine learning category and cites the breadth and depth of service as a strong point.

    However, Gartner rates the company a “Niche Player” in its MQ. It noted that SAP has made considerable strides in the data and machine learning spaces but still lags behind others in terms of features and capabilities.

    Read their in-depth review of SAP LeonardoSee user reviews of SAP Leonardo

    SAS Visual Data Mining and Machine Learning

    A pioneer in the data mining and analytics spaces, SAS has established itself as a top choice among machine learning vendors. It offers Visual Data Mining and Machine Learning (VDMML), which ranked at the standout “Leader” in the Forrester Wave and as a top “Leader” in the Gartner MQ, though the research firm notes that the overall platform has lost some ground in terms of vision and ability to execute.

    VDMML connects to numerous other products and data sources. Forrester noted that SAS has emerged as the first truly multimodal PAML solution. It uses a wizard to automatically train a model, convert it to code and plug in integrated features for automated machine learning. The unified environment also includes robust visual tools for creating models as well as deep neural networks.

    In addition, data scientists can embed snippets of code directly into SAS programs to further enhance capabilities, including machine learning and deep learning output.

    Read their in-depth review of SAS Visual Data Mining and Machine LearningSee user reviews of SAS Visual Data Mining and Machine Learning

    Honorable Mentions

    In addition to their top picks, there are other promising tools out there.

    Splunk

    Splunk Enterprise provides a broad based platform that can be used for searching, monitoring, and analyzing data. The software can import data from a variety of sources, from logs to Big Data sources.

    Data Science and Machine Learning Vendors at a Glance:

    Vendor and features

    Alteryx

    AWS SageMaker

    Google Machine Learning Engine

    IBM Watson Studio

    Microsoft Azure Machine Learning

    RapidMiner

    SAP Leonardo

    SAS Visual Data Mining and Machine Learning

    ML Focus

    Analytics and ML platform that allows users to build models in a single workflow

    Hosted platform that supports supervised and unsupervised ML and deep learning.

    Hosted platform that runs ML training jobs and predictions at scale.

    Broad data science focus with cloud and desktop ML platforms.

    Automated ML platform running on hosted Azure Cloud.

    Highly automated ML platform suited for firms aiming to use machine learning broadly.

    Broad and deep data science and ML platforms.

    Multimodal PAML solution with powerful wizards to support data mining and ML. Strong automation.

    Key Features and capabilities

    Offers more than 250 code free pre-built tools and supports open source tools and scripting.

    Zero-setup integrated Jupyter authoring notebook instance. Access to rich array of open source frameworks and tools.

    Automatic resource provisioning and monitoring for CPUs, GPUs and TPUs. Strong open source support for tools and scripting languages.

    Strong visual recognition and natural classification tools. Flexible approach that incorporates open source tools. Connects to IBM SPSS Modeler.

    Strong descriptive, diagnostic, predictive and prescriptive analytics tools and capabilities. Strong support for open source ML tools and scripting.

    Offers more than 1,500 machine learning and data prep functions, and it supports more than 40 files types. Connects to Amazon S3 and Dropbox.

    Fully supports a BYOM approach. Strong open source integration. Runs in SAP’s HANA public cloud.

    Includes best practice templates and in-memory processing that supports supervised and unsupervised learning.

    User comments

    High ratings for simplicity and usability. Complaints about the lack of robust reporting and visualization features.

    High marks for the interface, configurability and usability. However, some say that the familiarity with AWS is crucial.

    High ratings for functionality and integration.

    Highly rated for features and capabilities. Some complaints revolving around using notebooks.

    High ratings at Gartner Peer Review. Some complaints about support.

    Among the highest rated data science and ML solutions. Users describe it as powerful and “revolutionary” though there are complaints about the lack of GPU support. 

    Customers give SAP high marks for usability and performance. Some complain about the lack of visualization tools for external data.

    High ratings. Users like the powerful features and capabilities built into the platform, though some remark that there is a substantial learning curve associated with the product.

    Pricing and licensing

    Tiered system based on products used. Ranges from $5,195 to $33,800 per seat annually.

    Complex tiered pricing model based on ML notebook instances and cloud resources consumed.

    Based on resources and training hours used. Ranges from 1 cent per hour to $31 per hour for various services.

    Tiered model from $99 per month per user to $6,000 per month per user or more at enterprise level.

    Tiered pricing based on users and usage. Ranges from near zero to tens of thousands per user annually.

    Tiered pricing ranging from $2,500 per user per year to upwards of $10,000 per user per year.

    Tiered model based on node hours consumed in the cloud. Billing blocks are charge in increments of €7.25.

    N/A



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