What happens after we train a model in a Jupyter notebook? It’s time to deploy it!In this talk, we’ll learn about putting ML models into production and deploying it as a web service. We’ll cover:

• Saving and loading models with pickle
• Serving the model with Flask
• Creating and managing virtual environments with Pipenv
• Packaging the service in Docker
• Deploying the model to the cloud with AWS Beanstalk

By the end of this session, you’ll be able to deploy any Scikit-Learn model to production.