The Conference for Machine Learning Innovation

Dockerize, Standardize, Deploy and Scale your own Machine Learning Model

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Development of machine learning models happens mostly in Jupyter Notebooks these days. To bring them into production, Data Scientists struggle sometimes. In this talk, I will show how to transfer your great Jupyter notebook into a docker image that allows you to train your model locally. This local model will also let you predict locally as a service. The nice benefit of this docker image is its scalability: you can for instance upload it to AWS Sagemaker and run it on any instance type you want. I will also show you how to implement a Prediction Endpoint as a callable API.

Behind the Tracks