Migrate Your Custom ML Models To Google Cloud In 3 Steps
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…
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