{"id":1183,"date":"2023-05-03T13:35:35","date_gmt":"2023-05-03T13:35:35","guid":{"rendered":"https:\/\/www.analyticsvidhya.com\/datahack-summit-2023\/?page_id=1183"},"modified":"2023-08-04T10:40:10","modified_gmt":"2023-08-04T05:10:10","slug":"mastering-mlops-from-concepts-to-implementation","status":"publish","type":"page","link":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/workshop\/mastering-mlops-from-concepts-to-implementation\/","title":{"rendered":"Mastering MLOps &#8211; The Art of Productionalizing ML Models"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">Machine Learning has become an integral part of businesses across all industries, from healthcare and finance to retail and manufacturing. However, developing and deploying Machine Learning models at scale can be a complex and challenging task. This is where Machine Learning Operations (MLOps) comes in. MLOps is an emerging field that aims to streamline the process of developing and deploying Machine Learning models by applying DevOps principles to Machine Learning workflows.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Throughout this 8-hour comprehensive workshop, we&#8217;ll guide you into the evolving realm of Machine Learning Operations (MLOps). We&#8217;ll delve into a deep understanding of its architecture, demonstrating the blueprint for successful implementation. We&#8217;ll shed light on its practical applications, demonstrating hands-on exercises with Natural Language Processing (NLP) and Computer Vision models. Emphasizing on practical knowledge, we will cover the deployment of MLOps using these data models, intended to bolster your productivity. Additionally, we&#8217;ll ensure you&#8217;re equipped with an MLOps toolkit, knowledge to pick the right platform, and are aware of the standard best practices for MLOps mastery. The workshop also aims to bust myths associated with MLOps, providing a comprehensive and clear understanding of this important field.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Workshop Highlights:<\/span><\/p>\n<h4><span style=\"font-weight: 400;\">Module 1: Succinct Prelude to MLOps<\/span><\/h4>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">What MLOps Is and Why It Matters<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Busting The Buzzwords: DevOps, AIOps, and MLOps<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Lifecycle of a Machine Learning Model<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Different levels in MLOps Deployment<\/span><\/li>\n<\/ul>\n<h4><span style=\"font-weight: 400;\">Module 2: The MLOps Sackmesser<\/span><\/h4>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">MLOps Toolkits for<\/span>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Model Development<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Tuning<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Deployment<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Monitoring<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h4><span style=\"font-weight: 400;\">Module 3: MLOps and Real World Scenarios<\/span><\/h4>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Hands-on: MLOps Deployment on a Structured Dataset<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Hands-on: MLOps Deployment with NLP<\/span>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Bonus: Brief Introduction to Foundation Models and Prompt Engineering<\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Hands-on: MLOps Deployment with Computer Vision<\/span><\/li>\n<\/ul>\n<h4><span style=\"font-weight: 400;\">Module 4: MLOps Governance<\/span><\/h4>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Feedback Loop<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Maturity<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data and Process Governance<\/span><\/li>\n<\/ul>\n<h4><span style=\"font-weight: 400;\">Module 5: From Here to Where<\/span><\/h4>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Power Up Your Productivity<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Learn to Pick the Right Platform<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Standard Practices for MLOps Mastery<\/span><\/li>\n<\/ul>\n<h4><strong><br \/>\nPrerequisites:<\/strong><\/h4>\n<ul>\n<li>System Requirement and Setup\n<ul>\n<li>Laptop with at least 8 GB RAM<\/li>\n<li>Linux OS (preferred OS)<\/li>\n<li>GitHub Account<\/li>\n<li>Google Colab<\/li>\n<li><a href=\"https:\/\/docs.docker.com\/desktop\/install\/mac-install\/\" target=\"_blank\" rel=\"noopener\">Docker Desktop installation<\/a> (install the package as per your OS)\n<ul>\n<li>Will be good to have 8 GB RAM available and 8 GB Storage<\/li>\n<\/ul>\n<\/li>\n<li><a href=\"https:\/\/cloud.google.com\/free\" target=\"_blank\" rel=\"noopener\">Google Cloud Platform Free Tier<\/a><\/li>\n<\/ul>\n<\/li>\n<li>Pre-reads\n<ul>\n<li>Familiarity with Python<\/li>\n<li>Understanding of Docker and Containers<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>Note: These are tentative details and are subject to change.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Machine Learning has become an integral part of businesses across all industries, from healthcare and finance to retail and manufacturing. However, developing and deploying Machine Learning models at scale can be a complex and challenging task. This is where Machine Learning Operations (MLOps) comes in. MLOps is an emerging field that aims to streamline the [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1617,"parent":890,"menu_order":4,"comment_status":"closed","ping_status":"closed","template":"workshop-detail.php","meta":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Mastering MLOps - The Art of Productionalizing ML Models - DataHack Summit 2023<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/workshop\/mastering-mlops-from-concepts-to-implementation\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Mastering MLOps - The Art of Productionalizing ML Models - DataHack Summit 2023\" \/>\n<meta property=\"og:description\" content=\"Machine Learning has become an integral part of businesses across all industries, from healthcare and finance to retail and manufacturing. However, developing and deploying Machine Learning models at scale can be a complex and challenging task. This is where Machine Learning Operations (MLOps) comes in. 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