Navigating Experiment Management in AI Systems

This chapter explores the complexities of designing data storage and retrieval systems for high throughput distributed systems, emphasizing the transition from full to eventual consistency. It highlights the significance of checkpoints for fault tolerance in model training, along with the necessity of maintaining data integrity. Additionally, the chapter discusses the evaluation processes for pre-trained models and the challenges of managing multiple agents in AI development.