Running AI End-to-End in the Cloud - Part One

Chapter Overview

This course, Running AI End-to-End in the Cloud, covers the importance of optimizing AI pipelines for better performance and efficiency. It introduces the concept of an end-to-end AI pipeline, which includes data collection, data ingestion, feature engineering, model training, and deployment. The course highlights the need to look at AI problems holistically, rather than focusing on individual components. It also discusses various optimization strategies, including AI software acceleration, system-level tuning, runtime parameter optimizations, workload scaling, and learning optimizations. The course provides real-world examples of AI workflows and demonstrates how to apply these optimization strategies to achieve significant performance boosts. By the end of the course, learners will be able to understand what constitutes an end-to-end AI pipeline, comprehend the importance of holistic optimization, and apply various optimization strategies to achieve efficient AI performance.