MLOps
MLOps (Machine Learning Operations) is the practice of applying DevOps principles to machine learning systems, focusing on automating and streamlining the deployment, monitoring, and maintenance of ML models in production. It bridges the gap between data science and IT operations to ensure reliable, scalable, and reproducible ML workflows.
Companies need MLOps now because the shift from experimental ML to production-grade systems requires robust pipelines for continuous integration, delivery, and monitoring of models. With AI regulations tightening and model drift becoming a critical issue, businesses like Dataiku, Databricks, and Stripe prioritize MLOps to maintain compliance, reduce downtime, and scale AI solutions efficiently.
🎓 Courses
Machine Learning Engineering for Production (MLOps)
Andrew Ng's 4-course specialization — data lifecycle, modeling, deployment, monitoring. The gold standard.
MLOps Zoomcamp
Free hands-on course — MLflow, Prefect, Docker, model deployment. Project-based learning.
Full Stack Deep Learning
UC Berkeley course — ML project lifecycle, deployment, monitoring, team management. Free.
LLMOps
Google Cloud teaches LLM-specific ops — evaluation pipelines, prompt management.
Machine Learning DevOps Engineer Nanodegree
by Udacity
Industry-oriented program with Kubeflow, MLflow, Docker, and Kubernetes-based deployment
📖 Books
Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples
Andrew P. McMahon · 2023
This book provides practical, hands-on guidance for implementing MLOps pipelines and managing the full production lifecycle of ML models using Python.
Practical MLOps: Operationalizing Machine Learning Models
Noah Gift and Alfredo Deza · 2023
It focuses on the operational aspects of MLOps, offering concrete strategies and tools to deploy, monitor, and maintain machine learning models in production.
MLOps Engineering at Scale: Building Production-Ready Machine Learning Systems
Carl Osipov · 2024
This book tackles MLOps at scale, teaching you how to design and build robust, scalable infrastructure for machine learning systems in enterprise environments.
🛠️ Tutorials & Guides
MLflow Documentation
The most popular experiment tracking and model registry — logging, versioning, serving.
Weights & Biases Docs
Experiment tracking, hyperparameter sweeps, model registry. Beautiful dashboards.
Made with ML
End-to-end MLOps course with code — design, develop, deploy, iterate. Free.
MLOps Guide (Chip Huyen)
ML systems design exercises and interview prep — practical MLOps thinking.
Intro to Machine Learning
Free — understand what you're deploying. Core ML concepts before operationalizing.
Intermediate Machine Learning
Free — pipelines, cross-validation, data leakage. Production ML pitfalls to avoid.
🏅 Certifications
Google Cloud Professional ML Engineer
Google Cloud · $200
60% of the exam is MLOps — automating pipelines, monitoring, serving models on Vertex AI. Top-tier credential.
AWS Certified ML Engineer — Associate
AWS · $150
AWS's new ML certification replacing the Specialty exam — SageMaker pipelines, deployment, monitoring.
Databricks Certified ML Professional
Databricks · $200
MLflow, Feature Store, distributed training, drift detection, and automated retraining on Databricks.
Learning resources last updated: March 30, 2026