Notes about Cutting-Edge Technologies and Everything

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...

Once an outsider category, cloud computing now powers every industry. Look no further than this year’s Forbes Cloud 100 list, the annual ranking of the world’s top private cloud companies, where this year's standouts are keeping businesses surviving—and thriving—from real estate to retail, data to design. Produced for the fifth consecutive year in partnership with Bessemer Venture Partners and Salesforce Ventures, the Cloud 100 recognizes standouts in tech’s hottest category from small startups to private-equity-backed giants, from Silicon Valley to Australia and Hong Kong. The companies on the list are selected for their growth, sales, valuation and culture, as well as a reputation score derived in consultation with 43 CEO judges and executives from their public-cloud-company peers. This year’s new No. 1 has set a record for shortest time running atop the list. Database leader Snowflake takes the top slot, up from No. 2 last year and just hours before graduating from the list by g...

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...