Building Continuous Integration Pipeline With Jupyter Notebooks on GCP

It is a common belief that Jupyter Notebooks and production are not compatible. This leads to a several consequences: * Just a very few ML models and experiments evolve to a production environment * It takes very long time to deliver a simpler model to production even for the most valuable and business critical ML models We do believe that this can be drastically improved by providing a way of using Jupyter Notebooks directly into a production environment. And in this talk we will show you how to do it.