Course Overview
This course covers the concept of running AI end-to-end in the cloud, focusing on understanding what constitutes an end-to-end AI pipeline, the importance of looking at the problem/performance holistically, and applying end-to-end AI optimization strategies. The course explores the various phases of an AI pipeline, including data collection, data ingestion, feature engineering, model training, and deployment. It also discusses the importance of optimizing AI workflows, including data+AI software acceleration, system-level tuning, runtime parameter optimizations, workload scaling, and learning optimizations. The course provides real-world examples of optimization strategies and techniques, including model quantization, pruning, and knowledge distillation, and demonstrates how to achieve significant performance improvements using optimized software libraries and frameworks. By the end of the course, learners will be able to understand the end-to-end AI pipeline, apply optimization strategies, and improve the efficiency and performance of AI applications in the cloud.
Share With
Chapter Overview
This course covers the concept of running AI end-to-end in the cloud, focusing on understanding what constitutes an end-to-end AI pipeline, the importance of looking at the problem/performance holistically, and applying end-to-end AI optimization strategies. The course explores the various phases of an AI pipeline, including data collection, data ingestion, feature engineering, model training, and deployment. It also discusses the importance of optimizing AI workflows, including data+AI software acceleration, system-level tuning, runtime parameter optimizations, workload scaling, and learning optimizations. The course provides real-world examples of optimization strategies and techniques, including model quantization, pruning, and knowledge distillation, and demonstrates how to achieve significant performance improvements using optimized software libraries and frameworks. By the end of the course, learners will be able to understand the end-to-end AI pipeline, apply optimization strategies, and improve the efficiency and performance of AI applications in the cloud.