Machine Learning Systems

Machine Learning Systems refer to the end-to-end infrastructure, pipelines, and architectures required to develop, deploy, monitor, and maintain machine learning models in production. This encompasses data ingestion, feature engineering, model training, serving, and continuous evaluation. It bridges the gap between theoretical ML models and practical, scalable applications that deliver business value.

Also known as: ML Systems, MLOps, Machine Learning Infrastructure, Production ML, ML Engineering

Why learn Machine Learning Systems?

Developers should learn about Machine Learning Systems to build robust, scalable, and maintainable ML applications, especially when moving beyond prototyping to production environments. This is crucial for roles in data engineering, ML engineering, or AI product development, where ensuring model reliability, performance, and integration with existing systems is key. Use cases include recommendation engines, fraud detection systems, and autonomous vehicles.

Learning Resources

• book: Designing Machine Learning Systems
• docs: MLOps: Machine Learning Operations
• course: Machine Learning Engineering Course by Coursera
• book: Building Machine Learning Powered Applications
• docs: ML Systems Design Interview Guide