Building end-to-end pipelines is becoming more important as many businesses realize that having a machine learning model is only one small step towards getting their ML-driven application into production.

Google Cloud offers a tool for training and deploying models at scale, Cloud AI Platform, which integrates with multiple orchestration tools like TensorFlow Extended and KubeFlow Pipelines (KFP). However, it is often the case that businesses have models which they have built in their own ecosystem using frameworks like scikit-learn and xgboost, and porting these models to the cloud can be complicated and time consuming.

Even for experienced ML practitioners on Google Cloud Platform (GCP), migrating a scikit-learn model (or equivalent) to AI Platform can take a long time due to all the boilerplate that is involved. ML Pipeline Generator is a tool that allows users to easily deploy existing ML models on GCP, where they can then benefit from serverless model training and deployment and a faster time to market for their solutions.

This blog will provide an overview of how this solution works and the expected user journey, and instructions for orchestrating a TensorFlow training job on AI Platform.

#google cloud platform #ai & machine learning #machine-learning