This report introduces practical concepts to help data scientists and application engineers operationalize ML models to drive real business change.
Explore a preview version of "Introducing MLOps" before its official release, including the first two chapters that uncover how to:
- Fulfill data science value by reducing friction throughout ML pipelines and workflows
- Constantly refine ML models through retraining, periodic tuning, and even complete remodeling to ensure long-term accuracy
- Design the ML Ops lifecycle to ensure that people-facing models are unbiased, fair, and explainable