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PyTorch for Production: Building and Deploying ML Systems

7 chapters · 33 lessons

1. 1. Tensor Mechanics and Shape Reasoning

   4 lessons

   Master tensor fundamentals, broadcasting rules, and shape debugging to prevent the most common PyTorch errors.

2. 2. Autograd and Gradient Flow

   5 lessons

   Understand how PyTorch tracks operations and computes gradients to debug training issues and build custom layers.

3. 3. Efficient Data Loading and Preprocessing

   5 lessons

   Design data pipelines that don't bottleneck training using DataLoader, transforms, and batching strategies.

4. 4. Training Loop Design and Optimization

   5 lessons

   Build robust training loops with proper gradient updates, learning rate scheduling, and checkpoint management.

5. 5. Performance Profiling and Model Optimization

   5 lessons

   Use PyTorch profiling tools to identify bottlenecks and apply optimization techniques for faster training and inference.

6. 6. Model Export and Deployment Patterns

   5 lessons

   Export PyTorch models to portable formats and integrate them into production serving systems.

7. 7. Distributed Training and Hardware Portability

   4 lessons

   Scale training across multiple devices and understand how PyTorch abstracts hardware differences.