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.