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Building Serverless Machine Learning Models in the Cloud


PyData DC 2016

You’ll learn how to efficiently design and train machine learning models in Python and deploy them to the cloud. This process reduces the development & operational efforts required to make your prototypes production-ready.

We will describe the main challenges faced by data scientists involved in deploying machine learning models into real production environments with specific references, examples of Python libraries, and multi-model systems requiring advanced features such as A/B testing and high scalability & availability.

While discussing the advantages and limitations of multiple deployment strategies in the cloud, we will focus on serverless computing (i.e. AWS Lambda) as a solution for simplifying your development & deployment workflows.

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