Production DL
Deep Learning System Design — Building End-to-End DL Pipelines
Building production ML systems requires understanding distributed training, mixed precision, monitoring, and deployment. From data parallelism to A/B testing, this covers the engineering side of deep learning at scale — turning research models into reliable, efficient products.
- Key point 1 — Data parallelism with AllReduce is the most common distributed training strategy
- Key point 2 — Mixed precision (FP16 + FP32) delivers 2x speedup with minimal accuracy loss
- Key point 3 — Monitoring data drift and concept drift ensures models stay accurate in production
"A model is only as good as the system that serves it."
Deep Learning Systems Design
Building production ML systems requires understanding distributed training, mixed precision, monitoring, and deployment. This covers the engineering side of deep learning at scale.
Distributed Data Parallelism
Model Parallelism
Mixed Precision Training
Gradient Accumulation
Communication Overhead
ML System Architecture
Monitoring
A/B Testing
PyTorch Implementation
System Design Patterns
Practice Exercises
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DDP training: Train ResNet-50 on 4 GPUs using DDP. Measure speedup vs. single GPU.
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Mixed precision: Compare FP32 vs AMP training time and memory on ImageNet.
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Pipeline parallelism: Implement pipeline parallelism for a 24-layer Transformer.
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Monitoring dashboard: Set up data drift monitoring with PSI for a production model.
Key Takeaways
What to Learn Next
-> Model Compression Make deep learning models fast and efficient for production deployment.
-> Neural Architecture Search Let AI design its own neural networks through automated search.
-> Self-Supervised Learning Learn useful representations from unlabeled data without manual annotation.
-> CNN Architecture Deep Dive Master convolutional layers, pooling, and modern CNN architectures.
-> Attention Mechanisms Discover how attention solves the information bottleneck in sequence models.
-> Vision Transformers Apply Transformer architecture to image recognition by treating patches as tokens.