{"id":1372,"date":"2023-05-17T20:13:31","date_gmt":"2023-05-17T20:13:31","guid":{"rendered":"https:\/\/www.analyticsvidhya.com\/datahack-summit-2023\/?page_id=1372"},"modified":"2023-07-19T19:07:45","modified_gmt":"2023-07-19T13:37:45","slug":"landing-models-in-production-at-scale","status":"publish","type":"page","link":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/landing-models-in-production-at-scale\/","title":{"rendered":"Landing Models in Production at Scale"},"content":{"rendered":"<p><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Coming from an experienced industry leader in Applied ML and ML Engineering, the session will look at various definitions and contextual semantics for MLOps, look into the different MLOps components, look at the critical area of model serving in detail and also discuss several case studies.\\n\\nThe presentation will highlight the various reasons for the chasm to production for ML models, articulate the importance of Dev-Prod reproducibility, look at various industry definitions of MLOps and present maturity of MLOps across different generations. The MLOps value-map and how different industry players are filling in the value will be shown. This will be followed by explaining important concepts of MLOps for Applied ML practitioners such as different types of data drift, model drift and concept drift. Model Serving will be described in detail from both batch and real-time angles, including concepts of scaling and real-time API.\\n\\nThere will be a section on careers in MLOps as well as different case studies in the industry highlighting the above concepts\\n\\nKey Takeaways:\\n\\n Understanding MLOps and its components\\n Theoretical Concepts in MLOps explained\\n Model Serving - in both batch and real-time contexts\\n Careers in MLOps\\n Real-world case - studies\\n\\n&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:17405,&quot;3&quot;:{&quot;1&quot;:0},&quot;5&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;6&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;7&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;8&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;9&quot;:1,&quot;10&quot;:1,&quot;11&quot;:3,&quot;12&quot;:0,&quot;17&quot;:1}\">Coming from an experienced industry leader in Applied ML and ML Engineering, the session will look at various definitions and contextual semantics for MLOps, look into the different MLOps components, look at the critical area of model serving in detail and also discuss several case studies.<\/span><\/p>\n<p>The presentation will highlight the various reasons for the chasm to production for ML models, articulate the importance of Dev-Prod reproducibility, look at various industry definitions of MLOps and present maturity of MLOps across different generations. The MLOps value-map and how different industry players are filling in the value will be shown. This will be followed by explaining important concepts of MLOps for Applied ML practitioners such as different types of data drift, model drift and concept drift. Model Serving will be described in detail from both batch and real-time angles, including concepts of scaling and real-time API.<\/p>\n<p>There will be a section on careers in MLOps as well as different case studies in the industry highlighting the above concepts<\/p>\n<p><strong>Key Takeaways:<\/strong><\/p>\n<ul>\n<li><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Coming from an experienced industry leader in Applied ML and ML Engineering, the session will look at various definitions and contextual semantics for MLOps, look into the different MLOps components, look at the critical area of model serving in detail and also discuss several case studies.\\n\\nThe presentation will highlight the various reasons for the chasm to production for ML models, articulate the importance of Dev-Prod reproducibility, look at various industry definitions of MLOps and present maturity of MLOps across different generations. The MLOps value-map and how different industry players are filling in the value will be shown. This will be followed by explaining important concepts of MLOps for Applied ML practitioners such as different types of data drift, model drift and concept drift. Model Serving will be described in detail from both batch and real-time angles, including concepts of scaling and real-time API.\\n\\nThere will be a section on careers in MLOps as well as different case studies in the industry highlighting the above concepts\\n\\nKey Takeaways:\\n\\n Understanding MLOps and its components\\n Theoretical Concepts in MLOps explained\\n Model Serving - in both batch and real-time contexts\\n Careers in MLOps\\n Real-world case - studies\\n\\n&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:17405,&quot;3&quot;:{&quot;1&quot;:0},&quot;5&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;6&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;7&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;8&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;9&quot;:1,&quot;10&quot;:1,&quot;11&quot;:3,&quot;12&quot;:0,&quot;17&quot;:1}\">Understanding MLOps and its components<br \/>\n<\/span><\/li>\n<li><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Coming from an experienced industry leader in Applied ML and ML Engineering, the session will look at various definitions and contextual semantics for MLOps, look into the different MLOps components, look at the critical area of model serving in detail and also discuss several case studies.\\n\\nThe presentation will highlight the various reasons for the chasm to production for ML models, articulate the importance of Dev-Prod reproducibility, look at various industry definitions of MLOps and present maturity of MLOps across different generations. The MLOps value-map and how different industry players are filling in the value will be shown. This will be followed by explaining important concepts of MLOps for Applied ML practitioners such as different types of data drift, model drift and concept drift. Model Serving will be described in detail from both batch and real-time angles, including concepts of scaling and real-time API.\\n\\nThere will be a section on careers in MLOps as well as different case studies in the industry highlighting the above concepts\\n\\nKey Takeaways:\\n\\n Understanding MLOps and its components\\n Theoretical Concepts in MLOps explained\\n Model Serving - in both batch and real-time contexts\\n Careers in MLOps\\n Real-world case - studies\\n\\n&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:17405,&quot;3&quot;:{&quot;1&quot;:0},&quot;5&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;6&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;7&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;8&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;9&quot;:1,&quot;10&quot;:1,&quot;11&quot;:3,&quot;12&quot;:0,&quot;17&quot;:1}\">Theoretical Concepts in MLOps explained<br \/>\n<\/span><\/li>\n<li><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Coming from an experienced industry leader in Applied ML and ML Engineering, the session will look at various definitions and contextual semantics for MLOps, look into the different MLOps components, look at the critical area of model serving in detail and also discuss several case studies.\\n\\nThe presentation will highlight the various reasons for the chasm to production for ML models, articulate the importance of Dev-Prod reproducibility, look at various industry definitions of MLOps and present maturity of MLOps across different generations. The MLOps value-map and how different industry players are filling in the value will be shown. This will be followed by explaining important concepts of MLOps for Applied ML practitioners such as different types of data drift, model drift and concept drift. Model Serving will be described in detail from both batch and real-time angles, including concepts of scaling and real-time API.\\n\\nThere will be a section on careers in MLOps as well as different case studies in the industry highlighting the above concepts\\n\\nKey Takeaways:\\n\\n Understanding MLOps and its components\\n Theoretical Concepts in MLOps explained\\n Model Serving - in both batch and real-time contexts\\n Careers in MLOps\\n Real-world case - studies\\n\\n&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:17405,&quot;3&quot;:{&quot;1&quot;:0},&quot;5&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;6&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;7&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;8&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;9&quot;:1,&quot;10&quot;:1,&quot;11&quot;:3,&quot;12&quot;:0,&quot;17&quot;:1}\">Model Serving &#8211; in both batch and real-time contexts<br \/>\n<\/span><\/li>\n<li><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Coming from an experienced industry leader in Applied ML and ML Engineering, the session will look at various definitions and contextual semantics for MLOps, look into the different MLOps components, look at the critical area of model serving in detail and also discuss several case studies.\\n\\nThe presentation will highlight the various reasons for the chasm to production for ML models, articulate the importance of Dev-Prod reproducibility, look at various industry definitions of MLOps and present maturity of MLOps across different generations. The MLOps value-map and how different industry players are filling in the value will be shown. This will be followed by explaining important concepts of MLOps for Applied ML practitioners such as different types of data drift, model drift and concept drift. Model Serving will be described in detail from both batch and real-time angles, including concepts of scaling and real-time API.\\n\\nThere will be a section on careers in MLOps as well as different case studies in the industry highlighting the above concepts\\n\\nKey Takeaways:\\n\\n Understanding MLOps and its components\\n Theoretical Concepts in MLOps explained\\n Model Serving - in both batch and real-time contexts\\n Careers in MLOps\\n Real-world case - studies\\n\\n&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:17405,&quot;3&quot;:{&quot;1&quot;:0},&quot;5&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;6&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;7&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;8&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;9&quot;:1,&quot;10&quot;:1,&quot;11&quot;:3,&quot;12&quot;:0,&quot;17&quot;:1}\">Careers in MLOps<br \/>\n<\/span><\/li>\n<li><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Coming from an experienced industry leader in Applied ML and ML Engineering, the session will look at various definitions and contextual semantics for MLOps, look into the different MLOps components, look at the critical area of model serving in detail and also discuss several case studies.\\n\\nThe presentation will highlight the various reasons for the chasm to production for ML models, articulate the importance of Dev-Prod reproducibility, look at various industry definitions of MLOps and present maturity of MLOps across different generations. The MLOps value-map and how different industry players are filling in the value will be shown. This will be followed by explaining important concepts of MLOps for Applied ML practitioners such as different types of data drift, model drift and concept drift. Model Serving will be described in detail from both batch and real-time angles, including concepts of scaling and real-time API.\\n\\nThere will be a section on careers in MLOps as well as different case studies in the industry highlighting the above concepts\\n\\nKey Takeaways:\\n\\n Understanding MLOps and its components\\n Theoretical Concepts in MLOps explained\\n Model Serving - in both batch and real-time contexts\\n Careers in MLOps\\n Real-world case - studies\\n\\n&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:17405,&quot;3&quot;:{&quot;1&quot;:0},&quot;5&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;6&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;7&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;8&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;9&quot;:1,&quot;10&quot;:1,&quot;11&quot;:3,&quot;12&quot;:0,&quot;17&quot;:1}\">Real-world case &#8211; studies<br \/>\n<\/span><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Coming from an experienced industry leader in Applied ML and ML Engineering, the session will look at various definitions and contextual semantics for MLOps, look into the different MLOps components, look at the critical area of model serving in detail and also discuss several case studies. The presentation will highlight the various reasons for the [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1374,"parent":1126,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"session-details.php","meta":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Landing Models in Production at Scale - 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\/session\/landing-models-in-production-at-scale\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Landing Models in Production at Scale - DataHack Summit 2023\" \/>\n<meta property=\"og:description\" content=\"Coming from an experienced industry leader in Applied ML and ML Engineering, the session will look at various definitions and contextual semantics for MLOps, look into the different MLOps components, look at the critical area of model serving in detail and also discuss several case studies. The presentation will highlight the various reasons for the [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/landing-models-in-production-at-scale\/\" \/>\n<meta property=\"og:site_name\" content=\"DataHack Summit 2023\" \/>\n<meta property=\"article:modified_time\" content=\"2023-07-19T13:37:45+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/wp-content\/uploads\/2023\/05\/production-to-scale.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"500\" \/>\n\t<meta property=\"og:image:height\" content=\"250\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/landing-models-in-production-at-scale\/\",\"url\":\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/landing-models-in-production-at-scale\/\",\"name\":\"Landing Models in Production at Scale - DataHack Summit 2023\",\"isPartOf\":{\"@id\":\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/#website\"},\"datePublished\":\"2023-05-17T20:13:31+00:00\",\"dateModified\":\"2023-07-19T13:37:45+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/landing-models-in-production-at-scale\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/landing-models-in-production-at-scale\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/landing-models-in-production-at-scale\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Session\",\"item\":\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Landing Models in Production at Scale\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/#website\",\"url\":\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/\",\"name\":\"DataHack Summit 2023\",\"description\":\"\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-US\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Landing Models in Production at Scale - DataHack Summit 2023","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/landing-models-in-production-at-scale\/","og_locale":"en_US","og_type":"article","og_title":"Landing Models in Production at Scale - DataHack Summit 2023","og_description":"Coming from an experienced industry leader in Applied ML and ML Engineering, the session will look at various definitions and contextual semantics for MLOps, look into the different MLOps components, look at the critical area of model serving in detail and also discuss several case studies. The presentation will highlight the various reasons for the [&hellip;]","og_url":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/landing-models-in-production-at-scale\/","og_site_name":"DataHack Summit 2023","article_modified_time":"2023-07-19T13:37:45+00:00","og_image":[{"width":500,"height":250,"url":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/wp-content\/uploads\/2023\/05\/production-to-scale.jpg","type":"image\/jpeg"}],"twitter_card":"summary_large_image","twitter_misc":{"Est. reading time":"1 minute"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/landing-models-in-production-at-scale\/","url":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/landing-models-in-production-at-scale\/","name":"Landing Models in Production at Scale - DataHack Summit 2023","isPartOf":{"@id":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/#website"},"datePublished":"2023-05-17T20:13:31+00:00","dateModified":"2023-07-19T13:37:45+00:00","breadcrumb":{"@id":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/landing-models-in-production-at-scale\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/landing-models-in-production-at-scale\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/landing-models-in-production-at-scale\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/"},{"@type":"ListItem","position":2,"name":"Session","item":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/"},{"@type":"ListItem","position":3,"name":"Landing Models in Production at Scale"}]},{"@type":"WebSite","@id":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/#website","url":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/","name":"DataHack Summit 2023","description":"","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"en-US"}]}},"_links":{"self":[{"href":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/wp-json\/wp\/v2\/pages\/1372"}],"collection":[{"href":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/wp-json\/wp\/v2\/comments?post=1372"}],"version-history":[{"count":9,"href":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/wp-json\/wp\/v2\/pages\/1372\/revisions"}],"predecessor-version":[{"id":2352,"href":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/wp-json\/wp\/v2\/pages\/1372\/revisions\/2352"}],"up":[{"embeddable":true,"href":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/wp-json\/wp\/v2\/pages\/1126"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/wp-json\/wp\/v2\/media\/1374"}],"wp:attachment":[{"href":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/wp-json\/wp\/v2\/media?parent=1372"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}