Why Your DevOps Skills Are Your MLOps
summary
Argues DevOps practices map directly to MLOps by showing concrete pipeline pieces and failures: 90% of models never reach production, a reproducible stack built from Git/GitLab CI, Docker, Kubernetes and Kubeflow plus GPUs/TPUs for training, and data/model versioning with DVC and MLflow. Implements CI/CD plus continuous training and automated retraining to handle data drift and model drift, packages experiments and hyperparameters, scales workers for parallel training, and uses Grafana for accuracy and latency monitoring — a working, end-to-end MLOps flow.
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chapters (6)
- 0:00MLOps vs DevOps
- 2:51Data and model versioning
- 6:15CI/CD and continuous training
- 9:05Monitoring in MLOps
- 10:54Tools and Kubernetes
- 13:22Experimenting with tools
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