Build and deploy a complete machine learning workflow entirely within Snowflake ML! Our new solution guide walks you through an end-to-end ML lifecycle. You'll learn to define and manage features with Snowflake Feature Store, train and optimize models using Snowflake ML APIs for hyperparameter optimization, manage versioning and lifecycle with Snowflake Model Registry, and track performance and drift with integrated ML Observability. This guide demonstrates how to build, deploy, serve, and monitor models in production with seamlessly integrated MLOps features, all on Snowflake. Explore the solution and streamline your ML workflows: https://lnkd.in/g92t2Qcq
Thanks for sharing
Awesome Mr. Mofokeng...Well done!
Don’t forget to split test your models before committing to a full deployment! 😏
This is a truly great tutorial. Thanks. I think you should do something with the containers tutorials, they are not that intuitive to snowflake administrators imo, due to the tasks of creating docker images outside snowflake. I know it's easy and well explained, but not all admins have the ability to create them (security issues for example)
Data Engineer | Apache Spark | Snowflake | Polars | Python | Scala
6dI think that there is improvement areas yet in observability because the current metric metrics are basics.