{"id":26883,"date":"2020-03-11T08:00:30","date_gmt":"2020-03-11T15:00:30","guid":{"rendered":"https:\/\/devblogs.microsoft.com\/dotnet\/?p=26883"},"modified":"2020-03-10T15:35:23","modified_gmt":"2020-03-10T22:35:23","slug":"what-do-you-want-to-see-next-in-ml-net","status":"publish","type":"post","link":"https:\/\/devblogs.microsoft.com\/dotnet\/what-do-you-want-to-see-next-in-ml-net\/","title":{"rendered":"What do you want to see next in ML.NET?"},"content":{"rendered":"<p><a href=\"https:\/\/dot.net\/ml\" rel=\"noopener noreferrer\" target=\"_blank\">ML.NET<\/a> is an open source and cross-platform machine learning framework made for .NET developers.<\/p>\n<p>Using ML.NET, you can stay in .NET to easily build and consume custom machine learning models for scenarios like sentiment analysis, price prediction, sales forecasting, recommendation, image classification, and more.<\/p>\n<p>Over the past six months, the team has been working hard on fixing bugs, improving documentation, and adding more features and capabilities based on user feedback. This includes:<\/p>\n<ul>\n<li>Enhancements for .NET Core 3.0<\/li>\n<li>Azure training for image classification in Model Builder<\/li>\n<li>Expanded support for ONNX export<\/li>\n<li>Database loader for model training directly against relational databases<\/li>\n<li>Simplified Image Classification API for training image classification models<\/li>\n<li>Support for ML.NET in Jupyter Notebooks<\/li>\n<\/ul>\n<p>Now we&#8217;d like to see how you&#8217;re using ML.NET and what features we can add and\/or improve to make the framework and tooling even better.<\/p>\n<p>Through the survey below, we would love to get feedback on how we can improve ML.NET. We will use your feedback to drive the direction of ML.NET and update our <a href=\"https:\/\/github.com\/dotnet\/machinelearning\/blob\/master\/ROADMAP.md\" rel=\"noopener noreferrer\" target=\"_blank\">roadmap<\/a>.<\/p>\n<p><div  class=\"d-flex justify-content-center\"><a class=\"cta_button_link btn-primary mb-24\" href=\"https:\/\/www.research.net\/r\/mlnet-survey\" target=\"_blank\">Take the survey<\/a><\/div><\/p>\n<p><a href=\"https:\/\/www.research.net\/r\/mlnet-survey\"><img decoding=\"async\" src=\"https:\/\/devblogs.microsoft.com\/dotnet\/wp-content\/uploads\/sites\/10\/2019\/08\/survey-illustration-300x181.png\" alt=\"\" width=\"300\" height=\"181\" class=\"aligncenter size-medium wp-image-24294\" srcset=\"https:\/\/devblogs.microsoft.com\/dotnet\/wp-content\/uploads\/sites\/10\/2019\/08\/survey-illustration-300x181.png 300w, https:\/\/devblogs.microsoft.com\/dotnet\/wp-content\/uploads\/sites\/10\/2019\/08\/survey-illustration.png 588w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/p>\n<p>Thanks!<\/p>\n<p>ML.NET team<\/p>\n","protected":false},"excerpt":{"rendered":"<p>ML.NET is an open source and cross-platform machine learning framework made for .NET developers. Using ML.NET, you can stay in .NET to easily build and consume custom machine learning models for scenarios like sentiment analysis, price prediction, sales forecasting, recommendation, image classification, and more. Over the past six months, the team has been working hard [&hellip;]<\/p>\n","protected":false},"author":721,"featured_media":58792,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[685],"tags":[],"class_list":["post-26883","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-dotnet"],"acf":[],"blog_post_summary":"<p>ML.NET is an open source and cross-platform machine learning framework made for .NET developers. Using ML.NET, you can stay in .NET to easily build and consume custom machine learning models for scenarios like sentiment analysis, price prediction, sales forecasting, recommendation, image classification, and more. Over the past six months, the team has been working hard [&hellip;]<\/p>\n","_links":{"self":[{"href":"https:\/\/devblogs.microsoft.com\/dotnet\/wp-json\/wp\/v2\/posts\/26883","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/devblogs.microsoft.com\/dotnet\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/devblogs.microsoft.com\/dotnet\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/dotnet\/wp-json\/wp\/v2\/users\/721"}],"replies":[{"embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/dotnet\/wp-json\/wp\/v2\/comments?post=26883"}],"version-history":[{"count":0,"href":"https:\/\/devblogs.microsoft.com\/dotnet\/wp-json\/wp\/v2\/posts\/26883\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/dotnet\/wp-json\/wp\/v2\/media\/58792"}],"wp:attachment":[{"href":"https:\/\/devblogs.microsoft.com\/dotnet\/wp-json\/wp\/v2\/media?parent=26883"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/dotnet\/wp-json\/wp\/v2\/categories?post=26883"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/dotnet\/wp-json\/wp\/v2\/tags?post=26883"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}