{"id":2835,"date":"2023-07-18T18:14:27","date_gmt":"2023-07-18T12:44:27","guid":{"rendered":"https:\/\/www.analyticsvidhya.com\/datahack-summit-2023\/?page_id=2835"},"modified":"2023-07-21T11:58:54","modified_gmt":"2023-07-21T06:28:54","slug":"ai-and-ml-lifecycles-from-build-to-deployment-automation-and-retraining-at-scale","status":"publish","type":"page","link":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/ai-and-ml-lifecycles-from-build-to-deployment-automation-and-retraining-at-scale\/","title":{"rendered":"AI and ML Lifecycles: From Build to Deployment, Automation, and Retraining at Scale"},"content":{"rendered":"<p><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance, scale, and cost efficiency. To add to the challenge there is no one size fits all solution and solutions are heavily dependent on the team size, AI domain, resource budgets, security, and cloud platform.\\n\\nThis session focuses on the core principles and design considerations while designing an end-to-end ML Ops pipeline. Different designs for version control, pipelines, training, and deployment will be discussed for both engineers and data scientists based on where they stand in their MLOps journey.&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}\">Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance, scale, and cost efficiency. To add to the challenge there is no one size fits all solution and solutions are heavily dependent on the team size, AI domain, resource budgets, security, and cloud platform.<\/span><\/p>\n<p>This session focuses on the core principles and design considerations while designing an end-to-end ML Ops pipeline. Different designs for version control, pipelines, training, and deployment will be discussed for both engineers and data scientists based on where they stand in their MLOps journey.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance, scale, and cost efficiency. To add to the [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":2836,"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>AI and ML Lifecycles: From Build to Deployment, Automation, and Retraining 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\/ai-and-ml-lifecycles-from-build-to-deployment-automation-and-retraining-at-scale\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI and ML Lifecycles: From Build to Deployment, Automation, and Retraining at Scale - DataHack Summit 2023\" \/>\n<meta property=\"og:description\" content=\"Managing and scaling ML workloads have never been a bigger challenge in the past. 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To add to the [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/ai-and-ml-lifecycles-from-build-to-deployment-automation-and-retraining-at-scale\/\" \/>\n<meta property=\"og:site_name\" content=\"DataHack Summit 2023\" \/>\n<meta property=\"article:modified_time\" content=\"2023-07-21T06:28:54+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/wp-content\/uploads\/2023\/07\/AI-and-ML-Lifecycles-From-Build-to-Deployment-Automation-and-Retraining-at-Scale-100.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\/ai-and-ml-lifecycles-from-build-to-deployment-automation-and-retraining-at-scale\/\",\"url\":\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/ai-and-ml-lifecycles-from-build-to-deployment-automation-and-retraining-at-scale\/\",\"name\":\"AI and ML Lifecycles: From Build to Deployment, Automation, and Retraining at Scale - DataHack Summit 2023\",\"isPartOf\":{\"@id\":\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/#website\"},\"datePublished\":\"2023-07-18T12:44:27+00:00\",\"dateModified\":\"2023-07-21T06:28:54+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/ai-and-ml-lifecycles-from-build-to-deployment-automation-and-retraining-at-scale\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/ai-and-ml-lifecycles-from-build-to-deployment-automation-and-retraining-at-scale\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/ai-and-ml-lifecycles-from-build-to-deployment-automation-and-retraining-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\":\"AI and ML Lifecycles: From Build to Deployment, Automation, and Retraining 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":"AI and ML Lifecycles: From Build to Deployment, Automation, and Retraining 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\/ai-and-ml-lifecycles-from-build-to-deployment-automation-and-retraining-at-scale\/","og_locale":"en_US","og_type":"article","og_title":"AI and ML Lifecycles: From Build to Deployment, Automation, and Retraining at Scale - DataHack Summit 2023","og_description":"Managing and scaling ML workloads have never been a bigger challenge in the past. 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