Distributed Machine Learning Patterns
"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."
Welcome to the comprehensive guide to building distributed machine learning systems that can handle large-scale data, complex models, and heavy production traffic.
Table of Contents
- What You'll Learn
- Who This Guide Is For
- How to Use This Guide
- Guide Structure
- Technologies Covered
- Prerequisites
1. What You'll Learn
In plain English: This guide teaches you how to build ML systems that work across multiple machines, handle massive datasets, and serve millions of predictions reliably.
In technical terms: You'll master distributed training patterns (parameter servers, collective communication), model serving strategies (replicated and sharded services), workflow orchestration, and production operations.
Why it matters: Modern ML applications require scale that single machines can't provide. Understanding these patterns is essential for building production-grade ML systems.
2. Who This Guide Is For
- Scale training to large datasets
- Deploy models for high throughput
- Build reliable ML pipelines
- Design ML infrastructure
- Manage distributed resources
- Implement operational patterns
- Design scalable ML systems
- Choose appropriate patterns
- Plan production deployments
3. How to Use This Guide
Learning Paths
| Your Goal | Recommended Path |
|---|---|
| Understand fundamentals | Chapters 1 → 2 → 3 |
| Scale model training | Chapters 2 → 3 |
| Deploy to production | Chapters 4 → 5 → 6 |
| Build complete systems | Chapters 7 → 8 → 9 |
4. Guide Structure
| 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 |
5. Technologies Covered
| Technology | Purpose | Why It Matters |
|---|---|---|
| TensorFlow | ML model building | Industry standard for distributed training |
| Kubernetes | Container orchestration | De facto standard for managing distributed apps |
| Kubeflow | ML workflows on K8s | Specialized ML tooling for Kubernetes |
| Argo Workflows | Pipeline orchestration | Reliable, scalable workflow management |
| Docker | Containerization | Consistent environments across machines |
6. Prerequisites
To get the most from this guide:
- Python programming (1+ years experience)
- Basic machine learning knowledge (training, inference concepts)
- Command line comfort
- Docker basics (images, containers)
Insight
Don't worry if you're new to distributed systems — we build up from first principles with plenty of analogies and examples.
Ready to begin? Start with Chapter 1: Introduction to Distributed ML Systems