Key Stakeholders:<\/strong> IT managers, data scientists, data analysts, database administrators, application developers, enterprise statisticians, machine learning directors and managers, existing enterprise Cloudera customers<\/p>\n

Why It Matters:<\/strong> As Cloudera continues its pivot towards becoming a full-service machine learning and analytics platform, its latest updates enhance its ability to retain existing customers of its commercial data lake and Hadoop distribution looking to expand into data science workflows, and attract net-new data science customers.<\/p>\n

Top Takeaway:<\/strong> Cloudera’s additions to its Data Science Workbench provide a more rigorous, scientific approach to data science than prior versions, and allow for speedier implementation of results into enterprise software applications.<\/p>\n

Cloudera’s announcements at Strata London in late May reflect the next steps in its transformation from a Hadoop distribution and commercial data lake into a full-service machine learning and analytics platform. Key to this transformation are two new capabilities in Cloudera Data Science Workbench: Experiments, which introduces versioning to DSW, and Models, which streamlines and standardizes the model deployment process. Both of these capabilities add rigor and reproducibility to the data science process.<\/p>\n

To read the full report, please download it from our research library<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Key Stakeholders: IT managers, data scientists, data analysts, database administrators, application developers, enterprise statisticians, machine learning directors and managers, existing enterprise Cloudera customers Why It Matters: As Cloudera continues its pivot towards becoming a full-service machine learning and analytics platform, its latest updates enhance its ability to retain existing customers of its commercial data lake […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_newsletter_tier_id":0,"footnotes":""},"categories":[2],"tags":[],"class_list":["post-1217","post","type-post","status-publish","format-standard","hentry","category-data-science"],"jetpack_sharing_enabled":true,"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/amalgaminsights.com\/wp-json\/wp\/v2\/posts\/1217","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/amalgaminsights.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/amalgaminsights.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/amalgaminsights.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/amalgaminsights.com\/wp-json\/wp\/v2\/comments?post=1217"}],"version-history":[{"count":1,"href":"https:\/\/amalgaminsights.com\/wp-json\/wp\/v2\/posts\/1217\/revisions"}],"predecessor-version":[{"id":13175,"href":"https:\/\/amalgaminsights.com\/wp-json\/wp\/v2\/posts\/1217\/revisions\/13175"}],"wp:attachment":[{"href":"https:\/\/amalgaminsights.com\/wp-json\/wp\/v2\/media?parent=1217"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/amalgaminsights.com\/wp-json\/wp\/v2\/categories?post=1217"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/amalgaminsights.com\/wp-json\/wp\/v2\/tags?post=1217"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}