Awesome Machine Learning System Design: Curated Resource Guide
Machine learning system design has become one of the most in-demand skills in software engineering. It sits at the intersection of ML engineering, distributed systems, data engineering, and DevOps, and it requires a breadth of knowledge that no single course or textbook covers comprehensively. The good news is that the community has produced an extraordinary collection of books, courses, open-source repositories, blog posts, and tools that, taken together, provide everything you need to master this discipline.
This guide curates the best resources for learning ML system design, organized by format and focus area. Whether you are preparing for a system design interview at a top tech company, transitioning from software engineering into ML engineering, or building production AI systems at your organization, this list will save you weeks of searching and point you directly to the highest-quality materials available.
For a hands-on introduction to ML architecture patterns, see our ML system design patterns guide.
Books
These are the foundational texts that every ML system designer should read. Each approaches the topic from a different angle, and together they provide comprehensive coverage.
Designing Machine Learning Systems by Chip Huyen
This is the single best book on ML system design as of 2026. Chip Huyen draws on her experience at NVIDIA, Snorkel AI, and Stanford to cover the full lifecycle of production ML: data engineering, feature engineering, model development, deployment, monitoring, and maintenance. The book excels at explaining the "why" behind architectural decisions, not just the "what." It covers data distribution shifts, continual learning, responsible AI, and the organizational challenges of ML adoption. If you read only one book on this list, make it this one.
Best for: Engineers who want a comprehensive, practical understanding of end-to-end ML systems.
Designing Data-Intensive Applications by Martin Kleppmann
While not specifically about ML, this book is essential reading for anyone designing systems that process large volumes of data, which includes every production ML system. Kleppmann provides the deepest treatment available of distributed systems fundamentals: replication, partitioning, consistency, batch processing, and stream processing. Understanding these concepts is a prerequisite for designing reliable data pipelines, feature stores, and distributed training infrastructure.
Best for: Engineers who want a rigorous understanding of the distributed systems foundations that ML systems are built on.
Machine Learning Engineering by Andriy Burkov
This book bridges the gap between ML research and ML engineering. Burkov covers the practical aspects of building ML systems that academic courses often skip: data collection strategies, feature engineering at scale, model debugging, deployment patterns, and monitoring. The writing is concise and direct, making it a good complement to Chip Huyen's more comprehensive treatment.
Best for: Data scientists transitioning into ML engineering roles who need to understand production concerns.
Building Machine Learning Powered Applications by Emmanuel Ameisen
Ameisen takes a product-focused approach to ML engineering, walking through the complete process of building an ML application from idea to deployment. The book uses a real application (an ML-powered writing assistant) as a running example, which makes abstract concepts concrete. It covers scope definition, data exploration, baseline models, iteration, deployment, and monitoring.
Best for: Engineers who learn best through end-to-end project walkthroughs rather than abstract principles.
Reliable Machine Learning by Cathy Chen, Martin Fowler, et al.
Published by O'Reilly, this book focuses specifically on the operational aspects of ML: reliability, testing, monitoring, and incident response. It brings SRE (Site Reliability Engineering) principles into the ML domain and provides practical guidance on making ML systems as reliable as traditional software systems.
Best for: Platform engineers and SREs who are responsible for the operational health of ML systems.
GitHub Repositories
The open-source community has produced several exceptional repositories that aggregate ML system design knowledge.
system-design-primer by Donne Martin (270k+ stars)
The most popular system design repository on GitHub. While it covers general system design rather than ML specifically, it provides essential background on scalability, caching, load balancing, databases, and networking that underlies all ML infrastructure. The visual diagrams and Anki flashcards make it particularly useful for interview preparation.
Link: github.com/donnemartin/system-design-primer
machine-learning-systems-design by Chip Huyen (10k+ stars)
A companion to Chip Huyen's book, this repository contains a structured approach to ML system design interviews. It includes a framework for approaching design problems, sample questions with solutions, and links to relevant case studies from major tech companies.
Link: github.com/chiphuyen/machine-learning-systems-design
ml-engineering by Stas Bekman (12k+ stars)
A comprehensive, open-source guide to ML engineering maintained by Stas Bekman (formerly at Hugging Face). It covers topics that other resources often skip: multi-GPU training, debugging distributed systems, memory optimization, and performance profiling. The sections on training large language models are especially valuable.
Link: github.com/stas00/ml-engineering
applied-ml by Eugene Yan (28k+ stars)
A curated list of papers and technical articles from companies applying ML in production. Organized by use case (search, recommendation, NLP, computer vision, etc.), this repository provides real-world examples of how Netflix, Spotify, Airbnb, Uber, and dozens of other companies have designed their ML systems. Reading these case studies is one of the best ways to develop practical intuition for ML system design.
Link: github.com/eugeneyan/applied-ml
awesome-production-machine-learning (16k+ stars)
A curated list of open-source tools for deploying, monitoring, versioning, and scaling ML systems in production. Organized by category (data pipelines, feature stores, model serving, monitoring, etc.), it provides a comprehensive map of the ML infrastructure tooling landscape.
Link: github.com/EthicalML/awesome-production-machine-learning
Courses and Tutorials
Structured courses provide a guided learning path that books and repositories cannot replicate.
Stanford CS329S: Machine Learning Systems Design
Taught by Chip Huyen at Stanford, this course covers the design and operation of ML systems in production. The lecture notes, assignments, and case studies are available online. Topics include data management, model deployment, monitoring, continual learning, and responsible AI. This is the gold standard for academic ML systems education.
Best for: Engineers who prefer structured, university-level instruction with depth and rigor.
Full Stack Deep Learning
An online course and bootcamp that covers the full lifecycle of building ML-powered products: project scoping, data management, training, deployment, and monitoring. The course is regularly updated with new content on LLMs, MLOps, and production best practices. The emphasis on practical tooling (Docker, CI/CD, cloud deployment) distinguishes it from more theoretical courses.
Best for: Engineers who want hands-on experience with the tools used in production ML.
Made With ML by Goku Mohandas
A free, comprehensive course covering MLOps from fundamentals to advanced topics. It walks through building a complete ML application with proper software engineering practices: testing, CI/CD, monitoring, and versioning. The course uses a real NLP project as a running example and covers both the ML and engineering aspects thoroughly.
Best for: Self-directed learners who want a free, high-quality, project-based learning experience.
Coursera MLOps Specialization by Andrew Ng
A multi-course specialization covering the production ML lifecycle: data pipelines, model validation, deployment with TFX, and monitoring. While it is tied to the TensorFlow ecosystem, the concepts are broadly applicable. Andrew Ng's clear teaching style makes complex topics accessible, and the hands-on labs provide practical experience.
Best for: Engineers who learn well through video lectures and structured assignments with deadlines.
Databricks Academy: Large Language Models
A free course series from Databricks covering LLM fundamentals, fine-tuning, RAG architectures, and production deployment. The courses include hands-on notebooks that run on Databricks, but the architectural concepts apply regardless of platform.
Best for: Engineers who need to get up to speed on LLM-specific system design quickly.
Blogs and Newsletters
Technical blogs from major tech companies provide invaluable insight into how production ML systems are actually built and operated at scale.
Netflix Tech Blog
Netflix has published extensively about their recommendation system architecture, A/B testing infrastructure, feature engineering, and model serving. Articles like "System Architectures for Personalization and Recommendation" and "Distributed Time Travel for Feature Generation" are essential reading for anyone designing recommendation systems or feature stores.
Uber Engineering Blog
Uber's ML engineering blog documents Michelangelo (their ML platform), real-time feature serving, experimentation infrastructure, and natural language processing systems. The posts are technically detailed and include architecture diagrams that show how components interact at scale.
Spotify Engineering Blog
Spotify shares detailed posts about their recommendation algorithms, audio ML models, and the infrastructure that supports them. Their work on content-based recommendations, podcast search, and music understanding provides excellent case studies in domain-specific ML system design.
The Gradient
An online publication covering AI research and its practical applications. The Gradient bridges the gap between academic ML research and industry practice, with articles that explain recent advances in the context of production systems.
Eugene Yan's Blog (eugeneyan.com)
Eugene Yan (Senior Applied Scientist at Amazon) writes some of the best long-form content on ML system design, applied ML, and career development. His posts on feature stores, recommendation systems, and ML system design interviews are widely cited. The writing is clear, opinionated, and grounded in real production experience.
The Pragmatic Engineer by Gergely Orosz
While not ML-specific, this newsletter covers system design, engineering culture, and technical leadership at a depth that is directly relevant to ML platform engineers. The posts on system design interviews are particularly valuable for preparation.
Papers
Academic and industry papers provide the theoretical foundations and cutting-edge techniques that inform production ML system design.
Hidden Technical Debt in Machine Learning Systems (Google, 2015)
The foundational paper on ML system complexity. It introduces the concept of "ML-specific technical debt" and catalogs the maintenance challenges unique to ML systems: data dependencies, configuration debt, feedback loops, and underutilized experimental codepaths. Every ML engineer should read this paper.
Scaling Data-Centric AI: A Future-Proof Approach (MIT, 2023)
This paper makes the case for investing in data quality over model complexity. It provides frameworks for evaluating data quality, strategies for systematic data improvement, and evidence that better data often matters more than better algorithms.
TFX: A TensorFlow-Based Production-Scale Machine Learning Platform (Google, 2017)
Describes Google's end-to-end ML platform, covering data validation, feature engineering, model training, evaluation, and serving. While the specific technology is TensorFlow-centric, the architectural patterns apply broadly.
Feature Store for Machine Learning (Tecton, 2021)
A comprehensive treatment of feature store architecture, covering online and offline stores, feature computation, point-in-time correctness, and feature sharing across teams. Essential reading for anyone designing or evaluating feature store solutions.
Attention Is All You Need (Google, 2017)
The original transformer paper. While this is a model architecture paper rather than a systems paper, understanding the transformer architecture is essential for designing systems that serve, fine-tune, and scale LLMs.
Tools and Frameworks
The ML infrastructure tooling landscape is vast. Here are the most established tools, organized by category.
Orchestration
| Tool | Description | Best For |
|---|---|---|
| Apache Airflow | General-purpose workflow orchestrator, widely adopted | Teams with existing Airflow infrastructure |
| Dagster | Modern data orchestrator with strong typing and testing | Teams that value developer experience |
| Prefect | Python-native orchestration with minimal boilerplate | Small teams that want simplicity |
| Kubeflow Pipelines | ML-specific orchestration on Kubernetes | Teams heavily invested in Kubernetes |
Experiment Tracking
| Tool | Description | Best For |
|---|---|---|
| Weights & Biases | Best-in-class visualization and collaboration | Teams that value UX and team features |
| MLflow Tracking | Open-source, self-hostable experiment tracking | Teams that need full control over their data |
| Neptune.ai | Managed experiment tracking with strong integrations | Teams running many concurrent experiments |
| Comet ML | Experiment tracking with model production monitoring | Teams that want tracking and monitoring in one tool |
Model Serving
| Tool | Description | Best For |
|---|---|---|
| NVIDIA Triton | High-performance, multi-framework inference server | Production GPU serving at scale |
| vLLM | Optimized LLM serving with PagedAttention | LLM inference with high throughput |
| TorchServe | PyTorch-native model serving | Teams primarily using PyTorch |
| Seldon Core | Kubernetes-native model serving with A/B testing | Teams running on Kubernetes |
| BentoML | Python-first model serving framework | Teams that want fast prototyping |
Monitoring
| Tool | Description | Best For |
|---|---|---|
| Arize AI | ML-specific observability platform | Teams with many models in production |
| WhyLabs | Data and model monitoring with drift detection | Teams focused on data quality |
| Evidently AI | Open-source ML monitoring and testing | Teams that want open-source solutions |
| Grafana + Prometheus | General-purpose monitoring adapted for ML | Teams with existing Grafana infrastructure |
Feature Stores
| Tool | Description | Best For |
|---|---|---|
| Feast | Open-source feature store with online/offline serving | Teams that want open-source and flexibility |
| Tecton | Managed feature platform with streaming support | Teams that need real-time features |
| Databricks Feature Store | Integrated with the Databricks lakehouse | Teams already on Databricks |
| Hopsworks | Open-source feature store with built-in monitoring | Teams that want a self-hosted solution |
Interview Preparation Resources
ML system design is now a standard interview topic at major tech companies. These resources are specifically designed for interview preparation.
Books for Interview Prep
- System Design Interview (Vol 1 and 2) by Alex Xu covers general system design with clear diagrams and structured approaches. While not ML-specific, the methodology applies directly to ML system design interviews.
- Machine Learning System Design Interview by Ali Aminian and Alex Xu is the most targeted resource for ML-specific system design interviews, covering recommendation systems, ad prediction, search ranking, and other common ML interview questions.
- Designing Machine Learning Systems by Chip Huyen (mentioned above) provides the depth needed to handle follow-up questions that go beyond surface-level patterns.
Online Platforms
- Educative (Grokking the Machine Learning Interview) provides interactive, text-based courses on ML interview preparation with diagrams and quizzes.
- ByteByteGo by Alex Xu offers system design content through a newsletter, YouTube channel, and course platform with clean visual explanations.
- Exponent provides mock interview videos and structured guides for ML and system design interviews at specific companies.
Practice Approach
The most effective preparation combines structured study with active practice:
- Study patterns from the books and courses listed above
- Read case studies from the applied-ml repository to understand real-world implementations
- Practice with diagrams to build the skill of communicating architecture visually
- Mock interviews with peers, using the structured approach from Chip Huyen's repository
For a detailed study plan and preparation strategy, see our system design interview prep guide.
Communities
Learning ML system design is easier with a community. These are the most active and valuable communities for ML engineers.
MLOps Community
The largest community dedicated to ML operations, with an active Slack workspace, regular meetups, and a podcast. Members include ML engineers from major tech companies and startups, and discussions cover real-world production challenges, tool evaluations, and career advice.
Where to join: mlops.community
r/MachineLearning (Reddit)
The largest online community for ML discussion, with over 3 million members. While it skews toward research, the subreddit has significant discussion of production ML systems, tooling, and career topics. The weekly "What are you working on?" threads often surface interesting production ML projects.
Where to join: reddit.com/r/MachineLearning
AI Infrastructure Alliance
A community and vendor-neutral organization focused on the AI infrastructure ecosystem. They publish landscape maps of the ML tooling ecosystem, host events, and maintain working groups on topics like feature stores, model serving, and ML monitoring.
Where to join: ai-infrastructure.org
MLOps Slack Communities
Several tools maintain active Slack communities where you can get help from other practitioners:
- Great Expectations Slack for data quality
- Feast Slack for feature stores
- MLflow Slack for experiment tracking and model registry
- LangChain / LangGraph Discord for LLM application development
Practice Your ML System Design with InfraSketch
Reading about ML system design is essential, but the skill truly develops through practice. Designing architectures, drawing diagrams, and thinking through tradeoffs is how the concepts become second nature.
InfraSketch is an AI-powered system design tool that lets you describe an architecture in natural language and generates a complete diagram. You can then iterate on the design through conversation, adding components, modifying connections, and exploring tradeoffs.
Here are some practice exercises you can try:
- "Design a real-time recommendation system for an e-commerce platform" to practice the serving and feature store patterns
- "Design an MLOps pipeline for a fraud detection model" to practice training, evaluation, and monitoring patterns
- "Design a RAG system for a customer support chatbot" to practice LLM architecture patterns
- "Design a feature store that serves both batch training and real-time inference" to practice data platform patterns
Each prompt generates a starting diagram that you can refine through follow-up questions, building your intuition for how components connect and where the tradeoffs lie.
For a structured approach to learning these patterns, start with our complete guide to system design and then work through the more specialized guides on ML system design patterns and LLM system design architecture.