{"id":1452,"date":"2023-05-20T11:03:04","date_gmt":"2023-05-20T11:03:04","guid":{"rendered":"https:\/\/www.analyticsvidhya.com\/datahack-summit-2023\/?page_id=1452"},"modified":"2023-07-27T15:04:59","modified_gmt":"2023-07-27T09:34:59","slug":"my-ai-application-works-well-why-does-it-break-with-new-data-in-a-new-geo-next-year-help","status":"publish","type":"page","link":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/my-ai-application-works-well-why-does-it-break-with-new-data-in-a-new-geo-next-year-help\/","title":{"rendered":"My AI Application Works Well!  How Do I Keep it Working with New Data, in a New Geo, Next Year?"},"content":{"rendered":"<p><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Formulating business problems as AI\/ML use cases, finding the right datasets, creating models and fine tuning them. You took your AI Application through a PoC, pilot and customer deployment. You have been there, done that. Great! Now it breaks with new data or in the next geography or next year or at the next customer. What to do? We will share specific techniques through examples we have lived through, to keep the multiple levels of models improving, with novel model generation, automated evaluation, low-cost balanced metrics, and more. \\n\\nKey Takeaways: \\n - Identifying what breaks in the field\\n - Strategies to prevent such breakages\\n - Techniques for handling challenges such as distribution changes\\n - Methods to handle lack of direct visibility to business processes\\n - Explanation of metrics suitable for classification in imbalanced datasets\\n - Creation of models that handle different regimes of data variability separately\\n - Sharing our experience in ensuring apps functionality across:\\n Different geographical regions\\n Varying customer bases\\n Business changes\\n Different timeframes\\n \\nIn this session you'll learn how to put it all together for the enterprise. Come share your own experiences and challenges!&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:21501,&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;:0,&quot;10&quot;:1,&quot;11&quot;:3,&quot;12&quot;:0,&quot;15&quot;:&quot;\\&quot;Arial\\&quot;, sans-serif&quot;,&quot;17&quot;:1}\">Formulating business problems as AI\/ML use cases, finding the right datasets, creating models and fine tuning them. You took your AI Application through a PoC, pilot and customer deployment. You have been there, done that. Great! Now it breaks with new data or in the next geography or next year or at the next customer. What to do? We will share specific techniques through examples we have lived through, to keep the multiple levels of models improving, with novel model generation, automated evaluation, low-cost balanced metrics, and more.<\/span><\/p>\n<p><strong>Key Takeaways: <\/strong><\/p>\n<ul>\n<li><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Formulating business problems as AI\/ML use cases, finding the right datasets, creating models and fine tuning them. You took your AI Application through a PoC, pilot and customer deployment. You have been there, done that. Great! Now it breaks with new data or in the next geography or next year or at the next customer. What to do? We will share specific techniques through examples we have lived through, to keep the multiple levels of models improving, with novel model generation, automated evaluation, low-cost balanced metrics, and more. \\n\\nKey Takeaways: \\n - Identifying what breaks in the field\\n - Strategies to prevent such breakages\\n - Techniques for handling challenges such as distribution changes\\n - Methods to handle lack of direct visibility to business processes\\n - Explanation of metrics suitable for classification in imbalanced datasets\\n - Creation of models that handle different regimes of data variability separately\\n - Sharing our experience in ensuring apps functionality across:\\n Different geographical regions\\n Varying customer bases\\n Business changes\\n Different timeframes\\n \\nIn this session you'll learn how to put it all together for the enterprise. Come share your own experiences and challenges!&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:21501,&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;:0,&quot;10&quot;:1,&quot;11&quot;:3,&quot;12&quot;:0,&quot;15&quot;:&quot;\\&quot;Arial\\&quot;, sans-serif&quot;,&quot;17&quot;:1}\">Identifying what breaks in the field<br \/>\n<\/span><\/li>\n<li><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Formulating business problems as AI\/ML use cases, finding the right datasets, creating models and fine tuning them. You took your AI Application through a PoC, pilot and customer deployment. You have been there, done that. Great! Now it breaks with new data or in the next geography or next year or at the next customer. What to do? We will share specific techniques through examples we have lived through, to keep the multiple levels of models improving, with novel model generation, automated evaluation, low-cost balanced metrics, and more. \\n\\nKey Takeaways: \\n - Identifying what breaks in the field\\n - Strategies to prevent such breakages\\n - Techniques for handling challenges such as distribution changes\\n - Methods to handle lack of direct visibility to business processes\\n - Explanation of metrics suitable for classification in imbalanced datasets\\n - Creation of models that handle different regimes of data variability separately\\n - Sharing our experience in ensuring apps functionality across:\\n Different geographical regions\\n Varying customer bases\\n Business changes\\n Different timeframes\\n \\nIn this session you'll learn how to put it all together for the enterprise. Come share your own experiences and challenges!&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:21501,&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;:0,&quot;10&quot;:1,&quot;11&quot;:3,&quot;12&quot;:0,&quot;15&quot;:&quot;\\&quot;Arial\\&quot;, sans-serif&quot;,&quot;17&quot;:1}\">Strategies to prevent such breakages<br \/>\n<\/span><\/li>\n<li><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Formulating business problems as AI\/ML use cases, finding the right datasets, creating models and fine tuning them. You took your AI Application through a PoC, pilot and customer deployment. You have been there, done that. Great! Now it breaks with new data or in the next geography or next year or at the next customer. What to do? We will share specific techniques through examples we have lived through, to keep the multiple levels of models improving, with novel model generation, automated evaluation, low-cost balanced metrics, and more. \\n\\nKey Takeaways: \\n - Identifying what breaks in the field\\n - Strategies to prevent such breakages\\n - Techniques for handling challenges such as distribution changes\\n - Methods to handle lack of direct visibility to business processes\\n - Explanation of metrics suitable for classification in imbalanced datasets\\n - Creation of models that handle different regimes of data variability separately\\n - Sharing our experience in ensuring apps functionality across:\\n Different geographical regions\\n Varying customer bases\\n Business changes\\n Different timeframes\\n \\nIn this session you'll learn how to put it all together for the enterprise. Come share your own experiences and challenges!&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:21501,&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;:0,&quot;10&quot;:1,&quot;11&quot;:3,&quot;12&quot;:0,&quot;15&quot;:&quot;\\&quot;Arial\\&quot;, sans-serif&quot;,&quot;17&quot;:1}\">Techniques for handling challenges such as distribution changes<br \/>\n<\/span><\/li>\n<li><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Formulating business problems as AI\/ML use cases, finding the right datasets, creating models and fine tuning them. You took your AI Application through a PoC, pilot and customer deployment. You have been there, done that. Great! Now it breaks with new data or in the next geography or next year or at the next customer. What to do? We will share specific techniques through examples we have lived through, to keep the multiple levels of models improving, with novel model generation, automated evaluation, low-cost balanced metrics, and more. \\n\\nKey Takeaways: \\n - Identifying what breaks in the field\\n - Strategies to prevent such breakages\\n - Techniques for handling challenges such as distribution changes\\n - Methods to handle lack of direct visibility to business processes\\n - Explanation of metrics suitable for classification in imbalanced datasets\\n - Creation of models that handle different regimes of data variability separately\\n - Sharing our experience in ensuring apps functionality across:\\n Different geographical regions\\n Varying customer bases\\n Business changes\\n Different timeframes\\n \\nIn this session you'll learn how to put it all together for the enterprise. Come share your own experiences and challenges!&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:21501,&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;:0,&quot;10&quot;:1,&quot;11&quot;:3,&quot;12&quot;:0,&quot;15&quot;:&quot;\\&quot;Arial\\&quot;, sans-serif&quot;,&quot;17&quot;:1}\">Methods to handle lack of direct visibility to business processes<br \/>\n<\/span><\/li>\n<li><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Formulating business problems as AI\/ML use cases, finding the right datasets, creating models and fine tuning them. You took your AI Application through a PoC, pilot and customer deployment. You have been there, done that. Great! Now it breaks with new data or in the next geography or next year or at the next customer. What to do? We will share specific techniques through examples we have lived through, to keep the multiple levels of models improving, with novel model generation, automated evaluation, low-cost balanced metrics, and more. \\n\\nKey Takeaways: \\n - Identifying what breaks in the field\\n - Strategies to prevent such breakages\\n - Techniques for handling challenges such as distribution changes\\n - Methods to handle lack of direct visibility to business processes\\n - Explanation of metrics suitable for classification in imbalanced datasets\\n - Creation of models that handle different regimes of data variability separately\\n - Sharing our experience in ensuring apps functionality across:\\n Different geographical regions\\n Varying customer bases\\n Business changes\\n Different timeframes\\n \\nIn this session you'll learn how to put it all together for the enterprise. Come share your own experiences and challenges!&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:21501,&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;:0,&quot;10&quot;:1,&quot;11&quot;:3,&quot;12&quot;:0,&quot;15&quot;:&quot;\\&quot;Arial\\&quot;, sans-serif&quot;,&quot;17&quot;:1}\">Explanation of metrics suitable for classification in imbalanced datasets<br \/>\n<\/span><\/li>\n<li><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Formulating business problems as AI\/ML use cases, finding the right datasets, creating models and fine tuning them. You took your AI Application through a PoC, pilot and customer deployment. You have been there, done that. Great! Now it breaks with new data or in the next geography or next year or at the next customer. What to do? We will share specific techniques through examples we have lived through, to keep the multiple levels of models improving, with novel model generation, automated evaluation, low-cost balanced metrics, and more. \\n\\nKey Takeaways: \\n - Identifying what breaks in the field\\n - Strategies to prevent such breakages\\n - Techniques for handling challenges such as distribution changes\\n - Methods to handle lack of direct visibility to business processes\\n - Explanation of metrics suitable for classification in imbalanced datasets\\n - Creation of models that handle different regimes of data variability separately\\n - Sharing our experience in ensuring apps functionality across:\\n Different geographical regions\\n Varying customer bases\\n Business changes\\n Different timeframes\\n \\nIn this session you'll learn how to put it all together for the enterprise. Come share your own experiences and challenges!&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:21501,&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;:0,&quot;10&quot;:1,&quot;11&quot;:3,&quot;12&quot;:0,&quot;15&quot;:&quot;\\&quot;Arial\\&quot;, sans-serif&quot;,&quot;17&quot;:1}\">Creation of models that handle different regimes of data variability separately<br \/>\n<\/span><\/li>\n<li><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Formulating business problems as AI\/ML use cases, finding the right datasets, creating models and fine tuning them. You took your AI Application through a PoC, pilot and customer deployment. You have been there, done that. Great! Now it breaks with new data or in the next geography or next year or at the next customer. What to do? We will share specific techniques through examples we have lived through, to keep the multiple levels of models improving, with novel model generation, automated evaluation, low-cost balanced metrics, and more. \\n\\nKey Takeaways: \\n - Identifying what breaks in the field\\n - Strategies to prevent such breakages\\n - Techniques for handling challenges such as distribution changes\\n - Methods to handle lack of direct visibility to business processes\\n - Explanation of metrics suitable for classification in imbalanced datasets\\n - Creation of models that handle different regimes of data variability separately\\n - Sharing our experience in ensuring apps functionality across:\\n Different geographical regions\\n Varying customer bases\\n Business changes\\n Different timeframes\\n \\nIn this session you'll learn how to put it all together for the enterprise. Come share your own experiences and challenges!&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:21501,&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;:0,&quot;10&quot;:1,&quot;11&quot;:3,&quot;12&quot;:0,&quot;15&quot;:&quot;\\&quot;Arial\\&quot;, sans-serif&quot;,&quot;17&quot;:1}\">Sharing our experience in ensuring apps functionality across:<\/span>\n<ul>\n<li style=\"text-align: left;\"><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Formulating business problems as AI\/ML use cases, finding the right datasets, creating models and fine tuning them. You took your AI Application through a PoC, pilot and customer deployment. You have been there, done that. Great! Now it breaks with new data or in the next geography or next year or at the next customer. What to do? We will share specific techniques through examples we have lived through, to keep the multiple levels of models improving, with novel model generation, automated evaluation, low-cost balanced metrics, and more. \\n\\nKey Takeaways: \\n - Identifying what breaks in the field\\n - Strategies to prevent such breakages\\n - Techniques for handling challenges such as distribution changes\\n - Methods to handle lack of direct visibility to business processes\\n - Explanation of metrics suitable for classification in imbalanced datasets\\n - Creation of models that handle different regimes of data variability separately\\n - Sharing our experience in ensuring apps functionality across:\\n Different geographical regions\\n Varying customer bases\\n Business changes\\n Different timeframes\\n \\nIn this session you'll learn how to put it all together for the enterprise. Come share your own experiences and challenges!&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:21501,&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;:0,&quot;10&quot;:1,&quot;11&quot;:3,&quot;12&quot;:0,&quot;15&quot;:&quot;\\&quot;Arial\\&quot;, sans-serif&quot;,&quot;17&quot;:1}\">Different geographical regions<br \/>\n<\/span><\/li>\n<li style=\"text-align: left;\"><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Formulating business problems as AI\/ML use cases, finding the right datasets, creating models and fine tuning them. You took your AI Application through a PoC, pilot and customer deployment. You have been there, done that. Great! Now it breaks with new data or in the next geography or next year or at the next customer. What to do? We will share specific techniques through examples we have lived through, to keep the multiple levels of models improving, with novel model generation, automated evaluation, low-cost balanced metrics, and more. \\n\\nKey Takeaways: \\n - Identifying what breaks in the field\\n - Strategies to prevent such breakages\\n - Techniques for handling challenges such as distribution changes\\n - Methods to handle lack of direct visibility to business processes\\n - Explanation of metrics suitable for classification in imbalanced datasets\\n - Creation of models that handle different regimes of data variability separately\\n - Sharing our experience in ensuring apps functionality across:\\n Different geographical regions\\n Varying customer bases\\n Business changes\\n Different timeframes\\n \\nIn this session you'll learn how to put it all together for the enterprise. Come share your own experiences and challenges!&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:21501,&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;:0,&quot;10&quot;:1,&quot;11&quot;:3,&quot;12&quot;:0,&quot;15&quot;:&quot;\\&quot;Arial\\&quot;, sans-serif&quot;,&quot;17&quot;:1}\">Varying customer bases<br \/>\n<\/span><\/li>\n<li style=\"text-align: left;\"><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Formulating business problems as AI\/ML use cases, finding the right datasets, creating models and fine tuning them. You took your AI Application through a PoC, pilot and customer deployment. You have been there, done that. Great! Now it breaks with new data or in the next geography or next year or at the next customer. What to do? We will share specific techniques through examples we have lived through, to keep the multiple levels of models improving, with novel model generation, automated evaluation, low-cost balanced metrics, and more. \\n\\nKey Takeaways: \\n - Identifying what breaks in the field\\n - Strategies to prevent such breakages\\n - Techniques for handling challenges such as distribution changes\\n - Methods to handle lack of direct visibility to business processes\\n - Explanation of metrics suitable for classification in imbalanced datasets\\n - Creation of models that handle different regimes of data variability separately\\n - Sharing our experience in ensuring apps functionality across:\\n Different geographical regions\\n Varying customer bases\\n Business changes\\n Different timeframes\\n \\nIn this session you'll learn how to put it all together for the enterprise. Come share your own experiences and challenges!&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:21501,&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;:0,&quot;10&quot;:1,&quot;11&quot;:3,&quot;12&quot;:0,&quot;15&quot;:&quot;\\&quot;Arial\\&quot;, sans-serif&quot;,&quot;17&quot;:1}\">Business changes<\/span><\/li>\n<li style=\"text-align: left;\">Different timeframes<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Formulating business problems as AI\/ML use cases, finding the right datasets, creating models and fine tuning them. You took your AI Application through a PoC, pilot and customer deployment. You have been there, done that. Great! Now it breaks with new data or in the next geography or next year or at the next customer. What to do? We will share specific techniques through examples we have lived through, to keep the multiple levels of models improving, with novel model generation, automated evaluation, low-cost balanced metrics, and more. \\n\\nKey Takeaways: \\n - Identifying what breaks in the field\\n - Strategies to prevent such breakages\\n - Techniques for handling challenges such as distribution changes\\n - Methods to handle lack of direct visibility to business processes\\n - Explanation of metrics suitable for classification in imbalanced datasets\\n - Creation of models that handle different regimes of data variability separately\\n - Sharing our experience in ensuring apps functionality across:\\n Different geographical regions\\n Varying customer bases\\n Business changes\\n Different timeframes\\n \\nIn this session you'll learn how to put it all together for the enterprise. Come share your own experiences and challenges!&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:21501,&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;:0,&quot;10&quot;:1,&quot;11&quot;:3,&quot;12&quot;:0,&quot;15&quot;:&quot;\\&quot;Arial\\&quot;, sans-serif&quot;,&quot;17&quot;:1}\">In this session you&#8217;ll learn how to put it all together for the enterprise. Come share your own experiences and challenges!<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Formulating business problems as AI\/ML use cases, finding the right datasets, creating models and fine tuning them. You took your AI Application through a PoC, pilot and customer deployment. You have been there, done that. Great! Now it breaks with new data or in the next geography or next year or at the next customer. [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1453,"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>My AI Application Works Well! How Do I Keep it Working with New Data, in a New Geo, Next Year? - 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\/my-ai-application-works-well-why-does-it-break-with-new-data-in-a-new-geo-next-year-help\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"My AI Application Works Well! How Do I Keep it Working with New Data, in a New Geo, Next Year? - DataHack Summit 2023\" \/>\n<meta property=\"og:description\" content=\"Formulating business problems as AI\/ML use cases, finding the right datasets, creating models and fine tuning them. You took your AI Application through a PoC, pilot and customer deployment. You have been there, done that. Great! 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