Book Content
chapters • 8h36m total length
1. An Introduction to Pretraining Foundation Models
2. Dataset Preparation: Part One
3. Model Preparation
4. Containers and Accelerators on the Cloud
5. Distribution Fundamentals
6. Dataset Preparation: Part Two, the Data Loader
7. Finding the Right Hyperparameters
8. Large-Scale Training on SageMaker
9. Advanced Training Concepts
10. Fine-Tuning and Evaluating
11. Detecting, Mitigating, and Monitoring Bias
12. How to Deploy Your Model
13. Prompt Engineering
14. MLOps for Vision and Language
15. Future Trends in Pretraining Foundation Models
