A model’s accuracy will be at its best until you start using it. It then deteriorates as the world it was trained to predict in changes. MLOps pipelines need to prepare for change!

Join in to learn the best practices for ML pipeline automation to achieve continuous delivery of model prediction service using Streamlit, Google K8s & Git

Why Do ML Models Fail?

Implementing ML in a production environment ≠ deploying your model as an API for prediction. It means deploying an ML pipeline that can automate the retraining and deployment of new models.

Learn how you can use CD & automation pipelines in your ML pipelines to have:
- Active performance monitoring
- Understanding the cadence to retrain your production models
- Learn about templates for setting up robust ML pipelines with right triggers.

During the talk, I’ll cover Level 0 & Level 1 of automation for ML pipelines & discuss implementations of CI, CD and CT using Google's K8s and vortex AI