Model deployment is perhaps the most important step in the ML cycle. We have spent a lot of time and effort playing around with different algorithms, training, and tuning our model parameters, so after evaluating its performance and obtaining that long-awaited score, it is time to release it and show our model to the world. It sounds like graduation time!!, right?. However, the statistics show that 60% of the models never make it out into production, mainly because moving the model into a production environment is not simple and requires extra skills.

In this workshop, we will explore different deployment scenarios to release our ML models and learn how easy it is to move them to production using GCP(Google Cloud Platform).