11-part series
MLOps for DevOps Engineers
One evolving project, taken end to end across 11 parts. Follow it in order — each part builds on the last, from first principles to a system running in production.
Start with Part 1 →- 01Introduction to MLOps: The DevOps of Machine LearningPart 19 min read
- 02Serving ML Models with FastAPIPart 210 min read
- 03Containerizing ML Applications with DockerPart 310 min read
- 04CI/CD for MLOps with GitHub ActionsPart 410 min read
- 05Model Versioning & Experiment Tracking with MLflowPart 511 min read
- 06Data Versioning with DVC & S3Part 69 min read
- 07Deploying ML Applications on KubernetesPart 711 min read
- 08Infrastructure as Code for MLOps with TerraformPart 811 min read
- 09Monitoring ML Applications in ProductionPart 910 min read
- 10Data Drift, Model Drift & Automated RetrainingPart 1010 min read
- 11Build an End-to-End Production MLOps PlatformPart 1111 min read
