Description
Hear key learnings from building AI platforms for deploying ML models to production at scale. This includes a fully automated continuous delivery process and systematic measures to minimize the cost and effort required to sustain the models in production. The talk includes examples from different business domains and deployment scenarios covering their architecture which is based, in most cases, on streaming, microservice architecture optimized to be easily deployed with Docker containers and Kubernetes. This method offers a good separation of concerns as Data scientists don’t have to care about engineering aspects which are not part of their expertise. A data scientist can just push the model, which is code, while complying to some standards – and the rest will happen automagically. This code will be built, tested, deployed and activated in an AI platform that already has all the integration hooks to the biz domain. In addition, it offers cool manageability aspects that help track and maintain the model in production and reduce its total cost of ownership. This includes features such as applicative monitoring, model health indicators, re-train of models and more. Key Takeaways: 1…
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