Start here in MLOps
The most useful guides in this topic, not just the most recent.
MLOps Guides & Tutorials
Operationalise machine learning — MLflow, Docker, Kubernetes, CI/CD, monitoring and production ML system design.
MLOps articles
Building an End-to-End MLOps Pipeline
An end-to-end MLOps pipeline joins data validation, versioning, tracking, a registry, serving, monitoring and retraining into one loop. This guide runs that loop on real motorway traffic data: monitoring caught a summer surge, one retrained challenger was rejected, a second was promoted, and 2018 error fell 13%.
Cost Optimization for ML Workloads in the Cloud
Most of an ML cloud bill comes from three decisions: how capacity is bought, how fully it is used and how efficiently each prediction is made. With live Azure prices and two real models, this guide shows reservations and spot cutting prices by 38 to 82%, and an always-on VM costing seven times its compute.
Deploying ML Models on AWS, Azure and GCP
AWS, Azure and Google Cloud all host models behind managed endpoints, but each expects a different serving contract. This guide runs one real model behind all three contracts locally, including Microsoft's own inference server, and shows what changes between clouds and what should stay exactly the same.
From Data Scientist to MLOps Engineer: A Roadmap
Data scientists already know models, data and evaluation, which is most of what MLOps engineers wish their colleagues knew. The gap is software and operations. This plan sets out what transfers, what to learn, a twelve-week sequence built around one evolving project, and how to present the move to employers.
GPU vs CPU for ML: When You Actually Need a GPU
GPUs win on large, parallel maths and lose on small, scattered work. Measured on a laptop's integrated GPU and its CPU, the GPU ran at half the CPU's speed on tiny matrices, up to nine times faster on large ones, and nearly four times faster embedding sentences in batches, but slower one sentence at a time.
How to Build an MLOps Portfolio That Stands Out
An MLOps portfolio is judged on evidence that a system works and keeps working, not on how many tools it names. This guide covers what to build, what reviewers look for, how to structure the repository and README, how to use GitHub's profile features, and a rubric for checking your own project.