Practical Deep Learning at Scale with MLflow

Table of Contents

1. Deep Learning Life Cycle and MLOps Challenges
2. Getting Started with MLflow for Deep Learning
3. Tracking Models, Parameters, and Metrics
4. Tracking Code and Data Versioning
5. Running DL Pipelines in Different Environments
6. Running Hyperparameter Tuning at Scale
7. Multi-Step Deep Learning Inference Pipeline
8. Deploying a DL Inference Pipeline at Scale
9. Fundamentals of Deep Learning Explainability
10. Implementing DL Explainability with MLflow