If you are one of the Cool developer doing Style Transfer, Visual Translation or lurking at arxiv-sanity for what is hot, but wondering how would you take the model beyond Jupyter notebooks?


"It is my impression that the world of deep learning research is starting to plateau. What's booming: deploying DL to real-world problems."
-François Chollet


I trod the same path when I started as a founding ML Engineer, over the past two years I have learned that solid engineering is essential for building ML Application at web scale. Productionizing ML model is the last mile journey, the most dreaded and less talked about topic, knowing the right toolchain to automate your build pipeline is essential for APIfiying your ML Models.
Typical ML pipeline is accompanied by a big data infrastructure to de-normalize and preprocess the application data to prepare training data, then a microservice to expose the trained model artifact on a runtime component as a service.

In this session, we will explore the GCP DevOps toolchain to build, train, test, deploy and monitor an ML Model. The focus will be on the toolchain and how to automate the entire process from model development to deployment on Google Cloud Platform.