- The MLOps mindset
- Learning from decades of software engineering practices to build, test, and deploy models automatically at scale
- Distributed teams and collaboration
- How to evaluate, manage, test, and update a rising number of models across diverse use cases in organizations
- Practices to continuously operate models in environments with different personas, roles, and skill sets
- Collaboration practices and communication tools to maintain consistency and quality
- Achieving Full or Near-Full Automation of Model Updates
- When/why and how to automate model retraining and deployment in a dynamic data environment
- Risk management in automation: Detecting errors, performance drift, and maintaining compliance
- Model Governance Ecosystem tools
- Overview of MLOps tools ecosystem and the problem each one of them solves
- Building a sustainable, manageable MLOps framework that minimizes disruptions