{"id":620280,"date":"2020-02-28T22:12:06","date_gmt":"2019-12-07T23:25:29","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&#038;p=620280"},"modified":"2024-07-28T05:42:05","modified_gmt":"2024-07-28T12:42:05","slug":"flaml","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/flaml\/","title":{"rendered":"FLAML"},"content":{"rendered":"<section class=\"mb-3 moray-highlight\">\n\t<div class=\"card-img-overlay mx-lg-0\">\n\t\t<div class=\"card-background  has-background-grey card-background--inset-right\">\n\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1138\" height=\"451\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2020\/02\/flaml.png\" class=\"attachment-full size-full\" alt=\"logo, icon\" style=\"object-position: 48% 0%\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2020\/02\/flaml.png 1138w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2020\/02\/flaml-300x119.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2020\/02\/flaml-1024x406.png 1024w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2020\/02\/flaml-768x304.png 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2020\/02\/flaml-240x95.png 240w\" sizes=\"auto, (max-width: 1138px) 100vw, 1138px\" \/>\t\t<\/div>\n\t\t<!-- Foreground -->\n\t\t<div class=\"card-foreground d-flex mt-md-n5 my-lg-5 px-g px-lg-0\">\n\t\t\t<!-- Container -->\n\t\t\t<div class=\"container d-flex mt-md-n5 my-lg-5 \">\n\t\t\t\t<!-- Card wrapper -->\n\t\t\t\t<div class=\"w-100 w-lg-col-5\">\n\t\t\t\t\t<!-- Card -->\n\t\t\t\t\t<div class=\"card material-md-card py-5 px-md-5\">\n\t\t\t\t\t\t<div class=\"card-body \">\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\n<h1 class=\"wp-block-heading h2\" id=\"flaml\">FLAML<\/h1>\n\n\n\n<p>Accelerating development of machine learning applications for engineers and data scientists<\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n<\/section>\n\n\n\n\n\n<p class=\"has-text-align-left\"><strong>FLAML<\/strong> is a project for automating the machine learning model development process such as hyperparameter optimization and learner selection for better model quality or faster inference using low computational resource.<\/p>\n\n\n\n<p>More and more businesses start building millions of ML-embedded applications&#8211;it adds up to a large cost to manually choose the right training algorithm and tune the hyperparameters for every task and every dataset. Massive consumption of computation resources in tuning machine learning models also brings a tremendous burden to the environment.<\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"600\" height=\"636\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2020\/02\/AutoML_diagram-Jun2023.png\" alt=\"AutoML diagram\" class=\"wp-image-948006\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2020\/02\/AutoML_diagram-Jun2023.png 600w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2020\/02\/AutoML_diagram-Jun2023-283x300.png 283w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2020\/02\/AutoML_diagram-Jun2023-170x180.png 170w\" sizes=\"auto, (max-width: 600px) 100vw, 600px\" \/><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"600\" height=\"636\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2020\/02\/FLAML_diagram-Jun2023.png\" alt=\"FLAML diagram\" class=\"wp-image-948009\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2020\/02\/FLAML_diagram-Jun2023.png 600w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2020\/02\/FLAML_diagram-Jun2023-283x300.png 283w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2020\/02\/FLAML_diagram-Jun2023-170x180.png 170w\" sizes=\"auto, (max-width: 600px) 100vw, 600px\" \/><\/figure>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-columns are-vertically-aligned-top is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-vertically-aligned-top is-layout-flow wp-block-column-is-layout-flow\">\n<h3 class=\"wp-block-heading\" id=\"for-python-developers\">For Python developers<\/h3>\n\n\n\n<p>We built <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/microsoft.github.io\/FLAML\/\" target=\"_blank\" rel=\"noopener noreferrer\">FLAML<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, a fast library for AutoML and tuning, based on our research. It finds high quality models at your fingertips. It is easy to customize and extend. It tunes fast and as you like.<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-vertically-aligned-top is-layout-flow wp-block-column-is-layout-flow\">\n<h3 class=\"wp-block-heading\" id=\"for-net-developers\">For .NET developers<\/h3>\n\n\n\n<p>You can now access our technology from <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" style=\"font-size: calc(14.9px + 0.22vw)\" href=\"https:\/\/dotnet.microsoft.com\/apps\/machinelearning-ai\/ml-dotnet\/model-builder\" target=\"_blank\" rel=\"noopener noreferrer\">ML.NET Model Builder<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> in Visual Studio 2022. It provides an easy-to-understand visual interface to build, train, and deploy custom machine learning models in Visual Studio. Prior machine learning expertise is not required.<\/p>\n\n\n\n<p>You can also access our technology from <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/dot.net\/ml\" target=\"_blank\" rel=\"noopener noreferrer\">ML.NET<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> via code-first experience.<\/p>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<iframe loading=\"lazy\" src=\"https:\/\/ghbtns.com\/github-btn.html?user=microsoft&repo=FLAML&type=star&count=true&size=large\" frameborder=\"0\" scrolling=\"0\" width=\"170\" height=\"30\" title=\"GitHub\"><\/iframe>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<figure class=\"wp-block-image size-full is-style-spectrum\"><img loading=\"lazy\" decoding=\"async\" width=\"842\" height=\"720\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2020\/02\/highlight.png\" alt=\"text, letter\" class=\"wp-image-812761\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2020\/02\/highlight.png 842w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2020\/02\/highlight-300x257.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2020\/02\/highlight-768x657.png 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2020\/02\/highlight-211x180.png 211w\" sizes=\"auto, (max-width: 842px) 100vw, 842px\" \/><\/figure>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-16018d1d wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a data-bi-type=\"button\" class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/microsoft.github.io\/FLAML\/\">Documentation<\/a><\/div>\n<\/div>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>Read this <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/towardsdatascience.com\/fast-automl-with-flaml-ray-tune-64ff4a604d1c\" target=\"_blank\" rel=\"noopener noreferrer\">Anyscale Blog<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> to learn how it is integrated with Ray Tune to scale up distributed hyperparameter tuning.<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"wp-block-buttons is-content-justification-left is-content-justification-left is-layout-flex wp-container-core-buttons-is-layout-fdcfc74e wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a data-bi-type=\"button\" class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/devblogs.microsoft.com\/dotnet\/ml-net-june-updates\/#new-and-improved-automl\" target=\"_blank\" rel=\"noreferrer noopener\">FLAML in Model Builder<\/a><\/div>\n\n\n\n<div class=\"wp-block-button\"><a data-bi-type=\"button\" class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/devblogs.microsoft.com\/dotnet\/whats-new-with-mldotnet-automl\/\" target=\"_blank\" rel=\"noreferrer noopener\">FLAML in ML.NET<\/a><\/div>\n<\/div>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<figure class=\"wp-block-image size-large is-style-default\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"541\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2021\/08\/EconomicalAutoML-dotNET-bar-graph-1024x541.png\" alt=\".NET comparison bar graph: number of models explored comparison\" class=\"wp-image-770068\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2021\/08\/EconomicalAutoML-dotNET-bar-graph-1024x541.png 1024w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2021\/08\/EconomicalAutoML-dotNET-bar-graph-300x159.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2021\/08\/EconomicalAutoML-dotNET-bar-graph-768x406.png 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2021\/08\/EconomicalAutoML-dotNET-bar-graph-1536x812.png 1536w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2021\/08\/EconomicalAutoML-dotNET-bar-graph-240x127.png 240w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2021\/08\/EconomicalAutoML-dotNET-bar-graph.png 2029w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" src=\"https:\/\/devblogs.microsoft.com\/dotnet\/wp-content\/uploads\/sites\/10\/2021\/06\/automl-error-rate.png\" alt=\"error rate\" \/><\/figure>\n<\/div>\n<\/div>\n\n\n","protected":false},"excerpt":{"rendered":"<p>Accelerating development of machine learning applications for engineers and data 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