Master the art of building distributed machine learning systems with production-ready patterns
A comprehensive guide to building distributed machine learning systems that can handle large-scale data, complex models, and heavy production traffic.
Think of this like learning to build a restaurant chain instead of just cooking at home — you'll learn to coordinate multiple kitchens (machines), manage supply chains (data pipelines), and serve thousands of customers simultaneously.
- Distributed Training Patterns — Parameter servers, collective communication, synchronous and asynchronous training
- Model Serving Strategies — Replicated and sharded services, batch vs real-time inference
- Data Ingestion Patterns — Efficient data pipelines at scale
- Workflow Orchestration — Managing complex ML pipelines
- Production Operations — Monitoring, scaling, and reliability
| Role | What You'll Get |
|---|---|
| ML Engineers | Scale training to large datasets, deploy models for high throughput |
| Platform Engineers | Design ML infrastructure, manage distributed resources |
| Architects | Design scalable ML systems, choose appropriate patterns |
| Part | Chapters | Focus |
|---|---|---|
| I. Foundations | 1-2 | Introduction and data ingestion patterns |
| II. Core Patterns | 3-4 | Distributed training and model serving |
| III. Operations | 5-6 | Workflow and operation patterns |
| IV. Implementation | 7-9 | Architecture, technologies, and complete system |
- TensorFlow — Industry standard for distributed training
- Kubernetes — De facto standard for managing distributed apps
- Kubeflow — Specialized ML tooling for Kubernetes
- Argo Workflows — Reliable, scalable workflow management
- Docker — Consistent environments across machines
- Python programming (1+ years experience)
- Basic machine learning knowledge (training, inference concepts)
- Command line comfort
- Docker basics (images, containers)
Visit: https://YZXBiz.github.io/distributed-machine-learning/
cd docs
npm install
npm run startMIT