In the previous post, ten strategies to implement ai on the cloud and edge<\/a>, I discussed strategies for end to end deployment for machine learning modules.<\/p>\n <\/p>\n How this relates to Agile?<\/p>\n <\/p>\n Deployment of AI comes within the scope the normal SDLC (software development lifecycle)<\/p>\n So, normal Agile techniques like scrum, sprints, backlog planning, release planning, test driven development and acceptance criteria also apply to deployment of machine learning models in production.<\/p>\n <\/p>\n However, combining DevOps with development adds an extra level of complexity to machine learning. We need end to end pipelines and also CI\/CD (Continuous Integration\/Continuous Delivery)<\/p>\n <\/p>\n We introduce other issues such as the need for champion\/ challenger models<\/a><\/p>\n <\/p>\n However, the Cloud simplifies some of the complexity. For example – MLFlow (from databricks) is integrated into Azure<\/a> . Amazon sagemaker<\/a> provides an end-to-end workflow for managing machine learning models into production. Azure devops provides a good framework for Agile – Cloud – DevOps and even Edge into one framework. A good book I recommend is Agile Project Management with Azure DevOps<\/a><\/p>\n <\/p>\n An end to end view as below<\/p>\n<\/p>\n Diagram source Raj Sharma<\/a> as presented in the Artificial Intelligence Cloud and Edge implementations course<\/a><\/strong><\/p>\n <\/p>\n To conclude:<\/p>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<\/a><\/p>\n
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