Advanced MLOps Techniques
Cutting-edge approaches for next-generation MLOps.
Chapter 41: AutoMLOps AutoML integration Pipeline synthesis Tools: Google AutoML, H2O.ai Chapter 42: Continual Learning in Production Online learning Concept drift adaptation Techniques: Elastic Weight Consolidation, rehearsal Chapter 43: Multi-Model Systems Model routing Weighted ensembles Tools: MLflow Model Registry, Seldon Core Chapter 44: Reinforcement Learning in MLOps Applications: Recommendation systems, robotics Challenges: Exploration-exploitation trade-off