Infrastructure & Tooling - Full Stack Deep Learning - Spring 2021

Explore the core areas of ML infrastructure and tools landscape in this comprehensive lecture from the Full Stack Deep Learning Spring 2021 series. Gain insights into the contrast between the dream and reality for ML practitioners, and dive into the three main buckets of ML infrastructure and tooling. Learn about essential aspects such as software engineering, computing needs, resource management, frameworks, distributed training, experiment management, and hyperparameter optimization. Discover end-to-end solutions and get a thorough overview of tools and platforms for training and evaluation. The lecture covers topics ranging from introductory concepts to advanced techniques, providing a solid foundation for understanding the ML infrastructure ecosystem.

- Introduction
- The Dream vs. The Reality for ML Practitioners
- The 3 Buckets of ML Infrastructure/Tooling Landscape
- Software Engineering
- Compute Hardware
- Resource Management
- Frameworks and Distributed Training
- Experiment Management
- Hyperparameter Tuning
- "All-In-One" Solutions
- Follow us on Twitter for #ToolingTuesday! @full_stack_dl