Operationalizing Model Lifecycle with MLFlow.

If creating a production worthy model is hard, then managing the model lifecycle is even harder. There is a zoo of frameworks and tools that ML Practitioners can leverage.  Wouldn't it be nice to have a scalable framework to manage all these variations so that it can work  with any ML Library, Language and existing code! MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. There are various flavors of deploying models to suit a wide range of use cases including batch, streaming, realtime, edge, ensembles and many more. In this talk we will look at some common patterns used in the industry to not just build but also manage and govern models.

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