{"id":1637,"date":"2024-07-02T12:57:17","date_gmt":"2024-07-02T12:57:17","guid":{"rendered":"https:\/\/2024.automl.cc\/?page_id=1637"},"modified":"2024-09-09T12:15:56","modified_gmt":"2024-09-09T12:15:56","slug":"autogluon-towards-no-code-automated-machine-learning","status":"publish","type":"page","link":"https:\/\/2024.automl.cc\/?page_id=1637","title":{"rendered":"AutoGluon: Towards No-code Automated Machine Learning"},"content":{"rendered":"<div data-colibri-id=\"1637-c1\" class=\"style-754 style-local-1637-c1 position-relative\">\n  <!---->\n  <div data-colibri-component=\"section\" data-colibri-id=\"1637-c2\" id=\"custom\" class=\"h-section h-section-global-spacing d-flex align-items-lg-center align-items-md-center align-items-center style-759 style-local-1637-c2 position-relative\">\n    <!---->\n    <!---->\n    <div class=\"h-section-grid-container h-section-boxed-container\">\n      <!---->\n      <div data-colibri-id=\"1637-c3\" class=\"h-row-container gutters-row-lg-2 gutters-row-md-2 gutters-row-0 gutters-row-v-lg-2 gutters-row-v-md-2 gutters-row-v-2 style-760 style-local-1637-c3 position-relative\">\n        <!---->\n        <div class=\"h-row justify-content-lg-center justify-content-md-center justify-content-center align-items-lg-stretch align-items-md-stretch align-items-stretch gutters-col-lg-2 gutters-col-md-2 gutters-col-0 gutters-col-v-lg-2 gutters-col-v-md-2 gutters-col-v-2\">\n          <!---->\n          <div class=\"h-column h-column-container d-flex h-col-lg-auto h-col-md-auto h-col-auto style-761-outer style-local-1637-c4-outer\">\n            <div data-colibri-id=\"1637-c4\" class=\"d-flex h-flex-basis h-column__inner h-px-lg-2 h-px-md-2 h-px-2 v-inner-lg-2 v-inner-md-2 v-inner-2 style-761 style-local-1637-c4 position-relative\">\n              <!---->\n              <!---->\n              <div class=\"w-100 h-y-container h-column__content h-column__v-align flex-basis-100 align-self-lg-start align-self-md-start align-self-start\">\n                <!---->\n                <div data-colibri-id=\"1637-c5\" class=\"h-text h-text-component style-762 style-local-1637-c5 position-relative h-element\">\n                  <!---->\n                  <!---->\n                  <div class=\"\">\n                    <p><strong>Date:<\/strong>&nbsp;09.09.2024, 15:30-17:00<\/p>\n                    <p>Room: Auditorium<\/p>\n                    <h2>Speakers<\/h2>\n                    <ul>\n                      <li>\n                        <a href=\"https:\/\/caner.io\" style=\"color: rgb(3, 169, 244); font-size: 1em; font-weight: 400; font-family: &quot;Open Sans&quot;;\" class=\"customize-unpreviewable\">Caner T\u00fcrkmen<\/a>\n                      <\/li>\n                      <li>\n                        <a href=\"https:\/\/shchur.github.io\" style=\"color: rgb(3, 169, 244); font-size: 1em; font-weight: 400; font-family: &quot;Open Sans&quot;;\" class=\"customize-unpreviewable\">Oleksandr Shchur<\/a>\n                      <\/li>\n                      <li>\n                        <a href=\"https:\/\/www.linkedin.com\/in\/nickericksoncs\" style=\"color: rgb(3, 169, 244); font-size: 1em; font-weight: 400; font-family: &quot;Open Sans&quot;;\" class=\"customize-unpreviewable\">Nick Erickson<\/a>\n                      <\/li>\n                    <\/ul>\n                    <h2>Motivation<\/h2>\n                    <p>LLMs are not only transforming traditional AutoML approaches but also inspiring new applications like large pretrained models for time series forecasting. Moreover, researchers are looking to LLMs compose intelligent \u2018agents\u2019 capable\n                      of solving machine learning, and more broadly data science, tasks.&nbsp;<\/p>\n                    <p>\n                      <a href=\"https:\/\/auto.gluon.ai\" class=\"customize-unpreviewable\">AutoGluon<\/a> has been at the forefront of AutoML for over three years . The library has been consistently improving performance in tabular, multimodal, and time series forecasting problems, inspired by competitive machine learning\n                      solutions on platforms like Kaggle. Apart from building powerful machine learning solutions with LLMs and VLMs, the AutoGluon team has been focusing on new approaches enabled by large models.&nbsp;<\/p>\n                    <p>In this tutorial, we aim to both equip participants with the skills to use AutoGluon for machine learning tasks and to demonstrate how recent developments in ML are changing AutoML. The tutorial will start with an overview of AutoGluon.\n                      We will then dive into key design decisions behind AutoGluon-Tabular and AutoGluon-TimeSeries, in addition to providing usage examples. Next, we will discuss how large language model architectures are inspiring new applications in\n                      time series forecasting. We will provide a unifying overview of pretrained time series models, and introduce Chronos (\n                      <a href=\"https:\/\/arxiv.org\/abs\/2403.07815\" class=\"customize-unpreviewable\">Ansari et al., 2024<\/a>), the AutoGluon team\u2019s pretrained time series model. Finally, we will review autonomous data science agents powered by LLMs, capable of solving competitive ML problems end-to-end. We will discuss how these\n                      innovations are reshaping AutoML and enabling automated data science, and introduce the current approaches being explored by the AutoGluon team.<\/p>\n                    <h2>Outline<\/h2>\n                    <ol>\n                      <li>AutoGluon Overview\n                        <br>(a) AutoGluon-Tabular\n                        <br>(b) AutoGluon-TimeSeries<\/li>\n                      <li>Forecasting with Pretrained Time Series Models\n                        <br>(a) Overview of pretrained time series models\n                        <br>(b) Chronos: Learning the Language of Time Series\n                        <br>(c) Future directions<\/li>\n                      <li>Towards AutoML in Zero Lines of Code\n                        <br>(a) Overview of recent developments in ML agents\n                        <br>(b) AutoGluon-Assistant: An autonomous competitive ML Agent<\/li>\n                    <\/ol>\n                    <h2>Speakers<\/h2>\n                    <p>\n                      <br>\n                    <\/p>\n                  <\/div>\n                <\/div>\n              <\/div>\n            <\/div>\n          <\/div>\n        <\/div>\n      <\/div>\n    <\/div>\n  <\/div>\n  <div data-colibri-component=\"section\" data-colibri-id=\"1637-c6\" id=\"overlappable\" class=\"h-section h-section-global-spacing d-flex align-items-lg-center align-items-md-center align-items-center style-766 style-local-1637-c6 position-relative\">\n    <!---->\n    <!---->\n    <div class=\"h-section-grid-container h-section-boxed-container\">\n      <!---->\n      <div data-colibri-id=\"1637-c7\" class=\"h-row-container gutters-row-lg-0 gutters-row-md-0 gutters-row-0 gutters-row-v-lg-0 gutters-row-v-md-0 gutters-row-v-0 style-767 style-local-1637-c7 position-relative\">\n        <!---->\n        <div class=\"h-row justify-content-lg-center justify-content-md-center justify-content-center align-items-lg-stretch align-items-md-stretch align-items-stretch gutters-col-lg-0 gutters-col-md-0 gutters-col-0 gutters-col-v-lg-0 gutters-col-v-md-0 gutters-col-v-0\">\n          <!---->\n          <div class=\"h-column h-column-container d-flex h-col-lg-auto h-col-md-auto h-col-auto style-768-outer style-local-1637-c8-outer\">\n            <div data-colibri-id=\"1637-c8\" class=\"d-flex h-flex-basis h-column__inner h-ui-empty-state-container h-px-lg-0 h-px-md-0 h-px-0 v-inner-lg-0 v-inner-md-0 v-inner-0 style-768 style-local-1637-c8 position-relative\">\n              <!---->\n              <!---->\n              <div class=\"w-100 h-y-container h-column__content h-column__v-align flex-basis-100\">\n                <!---->\n              <\/div>\n            <\/div>\n          <\/div>\n          <div class=\"h-column h-column-container d-flex h-col-lg-auto h-col-md-auto h-col-auto style-769-outer style-local-1637-c9-outer\">\n            <div data-colibri-id=\"1637-c9\" class=\"d-flex h-flex-basis h-column__inner h-px-lg-2 h-px-md-2 h-px-2 v-inner-lg-2 v-inner-md-2 v-inner-2 style-769 style-local-1637-c9 position-relative\">\n              <!---->\n              <!---->\n              <div class=\"w-100 h-y-container h-column__content h-column__v-align flex-basis-100 align-self-lg-center align-self-md-center align-self-center\">\n                <!---->\n                <div data-colibri-id=\"1637-c10\" class=\"h-global-transition-all h-heading style-770 style-local-1637-c10 position-relative h-element\">\n                  <!---->\n                  <div class=\"h-heading__outer style-770 style-local-1637-c10\">\n                    <!---->\n                    <!---->\n                    <h4 class=\"\">Caner Turkmen<\/h4>\n                  <\/div>\n                <\/div>\n                <div data-colibri-id=\"1637-c11\" class=\"h-text h-text-component style-771 style-local-1637-c11 position-relative h-element\">\n                  <!---->\n                  <!---->\n                  <div class=\"\">\n                    <p>\n                      <a href=\"https:\/\/caner.io\" style=\"font-family: &quot;Open Sans&quot;; font-weight: 400; font-size: 1em; color: rgb(3, 169, 244);\" class=\"customize-unpreviewable\">Caner Turkmen<\/a>&nbsp;is a Senior Applied Scientist at Amazon Web Services, focusing on time series forecasting and AutoML. Before joining AWS, he worked as a data scientist in numerous management consulting firms and completed\n                      a PhD in machine learning at Bogazici University, Turkey. At AWS, he worked on multiple science teams before joining the AutoGluon team in 2022 and launching AutoGluon-TimeSeries. Caner previously gave invited talks and tutorials\n                      at the International Symposium on Forecasting and PyData Berlin 2023. His published works have appeared in AISTATS, AutoML Conference, IJCAI, JMLR, NeurIPS, among others.<\/p>\n                  <\/div>\n                <\/div>\n              <\/div>\n            <\/div>\n          <\/div>\n          <div class=\"h-column h-column-container d-flex h-col-lg-auto h-col-md-auto h-col-auto style-769-outer style-local-1637-c12-outer\">\n            <div data-colibri-id=\"1637-c12\" class=\"d-flex h-flex-basis h-column__inner h-px-lg-2 h-px-md-2 h-px-2 v-inner-lg-2 v-inner-md-2 v-inner-2 style-769 style-local-1637-c12 position-relative\">\n              <!---->\n              <!---->\n              <div class=\"w-100 h-y-container h-column__content h-column__v-align flex-basis-100 align-self-lg-center align-self-md-center align-self-center\">\n                <!---->\n                <div data-colibri-id=\"1637-c13\" class=\"h-global-transition-all h-heading style-770 style-local-1637-c13 position-relative h-element\">\n                  <!---->\n                  <div class=\"h-heading__outer style-770 style-local-1637-c13\">\n                    <!---->\n                    <!---->\n                    <h4 class=\"\">Oleksandr Shchur<\/h4>\n                  <\/div>\n                <\/div>\n                <div data-colibri-id=\"1637-c14\" class=\"h-text h-text-component style-771 style-local-1637-c14 position-relative h-element\">\n                  <!---->\n                  <!---->\n                  <div class=\"\">\n                    <p>\n                      <a href=\"https:\/\/shchur.github.io\" style=\"font-family: &quot;Open Sans&quot;; font-weight: 400; font-size: 1em; color: rgb(3, 169, 244);\" class=\"customize-unpreviewable\">Oleksandr Shchur<\/a>&nbsp;is an Applied Scientist at Amazon Web Services, where he works on time series forecasting in AutoGluon. Before joining AWS, he completed a PhD in Machine Learning at the Technical University of Munich, Germany,\n                      doing research on probabilistic models for event data. His research interests include machine learning for temporal data and generative modeling. Oleksandr is the lead developer of AutoGluon-TimeSeries and has given an invited talk\n                      on AutoGluon at PyData Berlin 2023. He has published papers at ICLR, ICML, IJCAI, and NeurIPS.<\/p>\n                  <\/div>\n                <\/div>\n              <\/div>\n            <\/div>\n          <\/div>\n          <div class=\"h-column h-column-container d-flex h-col-lg-auto h-col-md-auto h-col-auto style-774-outer style-local-1637-c15-outer\">\n            <div data-colibri-id=\"1637-c15\" class=\"d-flex h-flex-basis h-column__inner h-ui-empty-state-container h-px-lg-0 h-px-md-0 h-px-0 v-inner-lg-0 v-inner-md-0 v-inner-0 style-774 style-local-1637-c15 position-relative\">\n              <!---->\n              <!---->\n              <div class=\"w-100 h-y-container h-column__content h-column__v-align flex-basis-100\">\n                <!---->\n              <\/div>\n            <\/div>\n          <\/div>\n          <div class=\"h-column h-column-container d-flex h-col-lg-auto h-col-md-auto h-col-auto style-775-outer style-local-1637-c16-outer\">\n            <div data-colibri-id=\"1637-c16\" class=\"d-flex h-flex-basis h-column__inner h-ui-empty-state-container h-px-lg-2 h-px-md-2 h-px-2 v-inner-lg-2 v-inner-md-2 v-inner-2 style-775 style-local-1637-c16 position-relative\">\n              <!---->\n              <!---->\n              <div class=\"w-100 h-y-container h-column__content h-column__v-align flex-basis-100\">\n                <!---->\n              <\/div>\n            <\/div>\n          <\/div>\n        <\/div>\n      <\/div>\n    <\/div>\n  <\/div>\n  <div data-colibri-component=\"section\" data-colibri-id=\"1637-c17\" id=\"overlappable-2\" class=\"h-section h-section-global-spacing d-flex align-items-lg-center align-items-md-center align-items-center style-776 style-local-1637-c17 position-relative\">\n    <!---->\n    <!---->\n    <div class=\"h-section-grid-container h-section-boxed-container\">\n      <!---->\n      <div data-colibri-id=\"1637-c18\" class=\"h-row-container gutters-row-lg-0 gutters-row-md-0 gutters-row-0 gutters-row-v-lg-0 gutters-row-v-md-0 gutters-row-v-0 style-777 style-local-1637-c18 position-relative\">\n        <!---->\n        <div class=\"h-row justify-content-lg-center justify-content-md-center justify-content-center align-items-lg-stretch align-items-md-stretch align-items-stretch gutters-col-lg-0 gutters-col-md-0 gutters-col-0 gutters-col-v-lg-0 gutters-col-v-md-0 gutters-col-v-0\">\n          <!---->\n          <div class=\"h-column h-column-container d-flex h-col-lg-auto h-col-md-auto h-col-auto style-778-outer style-local-1637-c19-outer\">\n            <div data-colibri-id=\"1637-c19\" class=\"d-flex h-flex-basis h-column__inner h-ui-empty-state-container h-px-lg-0 h-px-md-0 h-px-0 v-inner-lg-0 v-inner-md-0 v-inner-0 style-778 style-local-1637-c19 position-relative\">\n              <!---->\n              <!---->\n              <div class=\"w-100 h-y-container h-column__content h-column__v-align flex-basis-100\">\n                <!---->\n              <\/div>\n            <\/div>\n          <\/div>\n          <div class=\"h-column h-column-container d-flex h-col-lg-auto h-col-md-auto h-col-auto style-779-outer style-local-1637-c20-outer\">\n            <div data-colibri-id=\"1637-c20\" class=\"d-flex h-flex-basis h-column__inner h-px-lg-2 h-px-md-2 h-px-2 v-inner-lg-2 v-inner-md-2 v-inner-2 style-779 style-local-1637-c20 position-relative\">\n              <!---->\n              <!---->\n              <div class=\"w-100 h-y-container h-column__content h-column__v-align flex-basis-100 align-self-lg-center align-self-md-center align-self-center\">\n                <!---->\n                <div data-colibri-id=\"1637-c21\" class=\"h-global-transition-all h-heading style-780 style-local-1637-c21 position-relative h-element\">\n                  <!---->\n                  <div class=\"h-heading__outer style-780 style-local-1637-c21\">\n                    <!---->\n                    <!---->\n                    <h4 class=\"\">Nick Erickson<\/h4>\n                  <\/div>\n                <\/div>\n                <div data-colibri-id=\"1637-c22\" class=\"h-text h-text-component style-781 style-local-1637-c22 position-relative h-element\">\n                  <!---->\n                  <!---->\n                  <div class=\"\">\n                    <p>\n                      <a href=\"https:\/\/www.linkedin.com\/in\/nickericksoncs\" style=\"font-family: &quot;Open Sans&quot;; font-weight: 400; font-size: 1em; color: rgb(3, 169, 244);\" class=\"customize-unpreviewable\">Nick Erickson<\/a>&nbsp;is a Senior Applied Scientist at Amazon AI. He obtained his master\u2019s degree in Computer Science and Engineering from the University of Minnesota Twin Cities. He is the author and lead developer of the open-source\n                      AutoML framework AutoGluon. Starting as a personal competition ML toolkit in 2018, Nick continually expanded the capabilities of AutoGluon and joined Amazon AI in 2019 to open-source the project and work full time on advancing the\n                      state-of-the-art in AutoML. Nick has given invited talks and tutorials on AutoGluon at ICML AutoML Workshop 2020 (Keynote), KDD 2020,ICML AutoML Workshop 2021, KDD 2022, AutoML 2022 (Keynote), AutoML Fall School 2022, NeurIPS 2022,\n                      PyData Seattle 2023, AutoML 2023, NeurIPS 2023. He has published papers on the topic of AutoML at ICML, NeurIPS, AutoML Conference, and JMLR.<\/p>\n                  <\/div>\n                <\/div>\n              <\/div>\n            <\/div>\n          <\/div>\n        <\/div>\n      <\/div>\n    <\/div>\n  <\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Date:&nbsp;09.09.2024, 15:30-17:00 Room: Auditorium Speakers Caner T\u00fcrkmen Oleksandr Shchur Nick Erickson Motivation LLMs are not only transforming traditional AutoML approaches but also inspiring new applications like large pretrained models for time series forecasting. Moreover, researchers are looking to LLMs compose intelligent \u2018agents\u2019 capable of solving machine learning, and more broadly data science, tasks.&nbsp; AutoGluon has [&hellip;]<\/p>\n","protected":false},"author":9,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/full-width-page.php","meta":{"footnotes":""},"class_list":["post-1637","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/2024.automl.cc\/index.php?rest_route=\/wp\/v2\/pages\/1637","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/2024.automl.cc\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/2024.automl.cc\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/2024.automl.cc\/index.php?rest_route=\/wp\/v2\/users\/9"}],"replies":[{"embeddable":true,"href":"https:\/\/2024.automl.cc\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1637"}],"version-history":[{"count":7,"href":"https:\/\/2024.automl.cc\/index.php?rest_route=\/wp\/v2\/pages\/1637\/revisions"}],"predecessor-version":[{"id":2563,"href":"https:\/\/2024.automl.cc\/index.php?rest_route=\/wp\/v2\/pages\/1637\/revisions\/2563"}],"wp:attachment":[{"href":"https:\/\/2024.automl.cc\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1637"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}