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This report assesses 20 vendors of platforms that data scientists and others can use to source data, build models and operationalize machine learning. It will help them make the right choice from a crowded field in a maturing DSML platform market that continues to show rapid product development. Market Definition/Description Gartner  defines a data science and machine learning (DSML) platform as a core product and supporting portfolio of coherently integrated products, components, libraries and frameworks (including proprietary, partner-sourced and open-source). Its primary users are data science professionals, including expert data scientists, citizen data scientists, data engineers, application developers and machine learning (ML) specialists. The core product and supporting portfolio: Are sufficiently well-integrated to provide a consistent “look and feel.” Create a user experience in which all components are reasonably interoperable in support of an analytics pipeline. The...

Expert data scientists and other professionals working in data science roles require capabilities to source data, build models and operationalize machine learning insights. Significant vendor growth, product development and myriad competing visions reflect a healthy market that is maturing rapidly. This Magic Quadrant evaluates vendors of data science and machine learning (DSML) platforms. Gartner defines a DSML platform as a core product and supporting portfolio of coherently integrated products, components, libraries and frameworks (including proprietary, partner and open source). Its primary users are data science professionals. These include expert data scientists, citizen data scientists, data engineers and machine learning (ML) engineers/specialists. Coherent integration means that the core product and supporting portfolio provide a consistent “look and feel” and create a user experience where all components are reasonably interoperable in support of an analytics pipel...

Data preparation tools have matured from initially being self-service-focused to now supporting data integration, analytics and data science use cases in production. Data and analytics leaders must use this research to understand the dynamics of and popular vendors in this rapidly evolving market. Key Findings The market for data preparation tools has evolved from being able to support only self-service use cases. Modern data preparation tools now enable data and analytics teams to build agile datasets at an enterprise scale, for a range of distributed content authors. The market for data preparation tools remains crowded and complex. The choices range from stand-alone specialists to vendors that embed data preparation — as a key capability — into their broader analytics/BI, data science or data integration tools. While most data preparation tool capabilities have been maturing at a steady state, organizations continue to cite “operationalization” — the ability to promote ...

Data science and machine-learning platforms enable organizations to take an end-to-end approach to building and deploying data science models. This Magic Quadrant evaluates 16 vendors to help you identify the right one for your organization's needs. Details >> (Provided by Alteryx here )

Data preparation — the most time-consuming task in analytics and BI — is evolving from a self-service activity to an enterprise imperative. We profile 28 data preparation tools for data and analytics leaders to consider to accelerate agile data preparation for a range of distributed content authors. Overview Key Findings The market for data preparation has now evolved from tools supporting only self-service use cases into platforms that enable data and analytics teams to build agile and searchable datasets at an enterprise scale for distributed content authors. Most vendor offerings support data profiling, data exploration, transformation, modeling and curation, and metadata support. More than 80% of the vendors surveyed embed some data cataloging features and offer varying degrees of machine-learning capabilities. The market is crowded with a range of choices, from stand-alone specialists to vendors that embed data preparation as a capability into analyti...

Details >> (provided by Tableau here )