data science platform

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Published By: TIBCO Software     Published Date: Aug 15, 2018
TIBCO Spotfire® Data Science is an enterprise big data analytics platform that can help your organization become a digital leader. The collaborative user-interface allows data scientists, data engineers, and business users to work together on data science projects. These cross-functional teams can build machine learning workflows in an intuitive web interface with a minimum of code, while still leveraging the power of big data platforms. Spotfire Data Science provides a complete array of tools (from visual workflows to Python notebooks) for the data scientist to work with data of any magnitude, and it connects natively to most sources of data, including Apache™ Hadoop®, Spark®, Hive®, and relational databases. While providing security and governance, the advanced analytic platform allows the analytics team to share and deploy predictive analytics and machine learning insights with the rest of the organization, white providing security and governance, driving action for the business.
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TIBCO Software
Published By: Trifacta     Published Date: Feb 12, 2019
Over the past few years, the evolution of technology for storing, processing and analyzing data has been absolutely staggering. Businesses now have the ability to work with data at a scale and speed that many of us would have never thought was possible. Yet, why are so many organizations still struggling to drive meaningful ROI from their data investments? The answer starts with people. In this latest Data Science Central webinar, guest speakers Forrester Principal Analyst Michele Goetz and Trifacta Director of Product Marketing Will Davis focus on the roles and responsibilities required for today’s modern dataops teams to be successful. They touch on how new data platforms and applications have fundamentally changed the traditional makeup of data/analytics organizations and how companies need to update the structure of their teams to keep up with the accelerate pace of modern business. Watch this recorded webcast to learn: What are the foundational roles within a modern dataops team a
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Trifacta
Published By: Group M_IBM Q2'19     Published Date: Apr 01, 2019
IBM Cloud Private for Data is an integrated data science, data engineering and app building platform built on top of IBM Cloud Private (ICP). The latter is intended to a) provide all the benefits of cloud computing but inside your firewall and b) provide a stepping-stone, should you want one, to broader (public) cloud deployments. Further, ICP has a micro-services architecture, which has additional benefits, which we will discuss. Going beyond this, ICP for Data itself is intended to provide an environment that will make it easier to implement datadriven processes and operations and, more particularly, to support both the development of AI and machine learning capabilities, and their deployment. This last point is important because there can easily be a disconnect Executive summary between data scientists (who often work for business departments) and the people (usually IT) who need to operationalise the work of those data scientists
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Group M_IBM Q2'19
Published By: Domino Data Lab     Published Date: Feb 08, 2019
A data science platform is where all data science work takes place and acts as the system of record for predictive models. While a few leading model-driven businesses have made the data science platform an integral part of their enterprise architecture, most companies are still trying to understand what a data science platform is and how it fits into their architecture. Data science is unlike other technical disciplines, and models are not like software or data. Therefore, a data science platform requires a different type of technology platform. This document provides IT leaders with the top 10 questions to ask of data science platforms to ensure the platform handles the uniqueness of data science work.
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Domino Data Lab
Published By: Domino Data Lab     Published Date: Feb 08, 2019
As organizations increasingly strive to become model-driven, they recognize the necessity of a data science platform. According to a recent survey report “Key Factors on the Journey to Become Model-Driven”, 86% of model-driven companies differentiate themselves by using a data science platform. And yet the question of whether to build or buy still remains. This paper presents a framework to facilitate the decision process, and considers the four-year projection of total costs for both approaches in a sample scenario. Read this whitepaper to understand three major factors in your decision process: Total cost of ownership - Internal build costs often run into the tens of millions Opportunity costs - Distraction from your core competency Risk factors - Missed deadlines and delayed time to market
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Domino Data Lab
Published By: MarkLogic     Published Date: May 07, 2018
Learn how Life Sciences organizations can accelerate Real World Evidence by achieving faster time to insight with a metadata-driven, semantically enriched operational platform. Real World Evidence (RWE) is today’s big data challenge in Life Sciences. Medical records, registries, consultation reports, insurance claims, pharmacy data, social media, and patient surveys all contain valuable insights that Life Sciences organizations need to ascertain and prove the safety, efficacy, and value of their drugs and medical devices. Learn how Life Sciences organizations can accelerate RWE with a metadata-driven, semantically enriched operational platform that enables them to: • Unify, harmonize and ensure governance of information from diverse data sources • Transform information into evidence that proves product efficacy and safety • Identify data patterns, connections, and relationships for faster time to insight
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data, integration, drug, device, manufacture, science
    
MarkLogic
Published By: Hortonworks     Published Date: Apr 05, 2016
The advent of big data revolutionized analytics and data science and created the concept of new data platforms, allowing enterprises to store, access and analyze vast amounts of historical data. The world of big data was born. But existing data platforms need to evolve to deal with the tsunami of data-in-motion being generated by the Internet of Anything (IoAT).
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Hortonworks
Published By: Alteryx, Inc.     Published Date: Sep 06, 2017
Gartner has published its "2017 Magic Quadrant for Data Science Platforms," and we are pleased to share a complimentary copy of this important research with you.
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Alteryx, Inc.
Published By: TIBCO Software     Published Date: Sep 21, 2018
BUSINESS CHALLENGE “Vestas is a global market leader in manufacturing and servicing wind turbines,” explains Sven Jesper Knudsen, Ph.D., senior data scientist. “Turbines provide a lot of data, and we analyze that data, adapt to changing needs, and work to create a best-in-class wind energy solution that provides the lowest cost of energy. “To stay ahead, we have created huge stacks of technologies—massive amounts of data storage and technologies to transform data with analytics. That comes at a cost. It requires maintenance and highly skilled personnel, and we simply couldn’t keep up. The market had matured, and to stay ahead we needed a new platform. “If we couldn’t deliver on time, we would let users and the whole business down, and start to lose a lot of money on service. For example, if we couldn’t deliver a risk report on time, decisions would be made without actually understanding the risk landscape.
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data solution, technology solution, data science, streaming data, fast data platform, self-service analytics
    
TIBCO Software
Published By: IBM     Published Date: Apr 07, 2017
Data science platforms are engines for creating machine-learning solutions. Innovation in this market focuses on cloud, Apache Spark, automation, collaboration and artificial-intelligence capabilities. We evaluate 16 vendors to help you make the best choice for your organization. This Magic Quadrant evaluates vendors of data science platforms. These are products that organizations use to build machine-learning solutions themselves, as opposed to outsourcing their creation or buying ready-made solutions.
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data analytics, product refinement, business exploration, advanced prototyping, analytics, data preparation, customer support, sales relations, market research, model management
    
IBM
Published By: Altiscale     Published Date: Aug 25, 2015
Weren't able to attend Hadoop Summit 2015? No sweat. Learn more about the latest Big Data technologies in these technical presentations at this recent leading industry event. The Big Data experts at Altiscale - the leader in Big Data as a Service - have been busy at conferences. To see all four presentations (in slides and youtube video), click here. https://www.altiscale.com/educational-slide-kit-2015-big-data-conferences-nf/ • Managing Growth in Production Hadoop Deployments • Running Spark & MapReduce Together in Production • YARN and the Docker Ecosystem • 5 Tips for Building a Data Science Platform
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hadoop, hadoop technologies, hadoop information
    
Altiscale
Published By: Group M_IBM Q1'18     Published Date: Feb 14, 2018
Data science platforms are engines for creating machine-learning solutions. Innovation in this market focuses on cloud, Apache Spark, automation, collaboration and artificial-intelligence capabilities. We evaluate 16 vendors to help you make the best choice for your organization.
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gartner, magic quadrant, data science platform, machine-learning
    
Group M_IBM Q1'18
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