MLflow serving is a great way to deploy any model as a rest API endpoint and start experimenting. But what about taking it to the next level? What if we want to deploy our application to production just like any other server in a containerized environment? What about adding custom middlewares, monitoring, logging and tweaking performance for high scale?

In this talk I will cover what we did in Yotpo in order to make MLflow serving production-grade!

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Databricks provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business.
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• Introducing MLflow for End-to-End Machine Learning on Databricks ( Download)
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• Productionizing Machine Learning with Apache Spark, MLflow and ONNX to the cloud using SQL Server ( Download)
• Accelerate Your ML Pipeline with AutoML and MLflow ( Download)
• An Intro to MLflow and Azure ML ( Download)