Improving Machine Learning Workflow
summary
Describes a Docker-based infrastructure that integrates MLflow, JupyterHub and Minio (S3-compatible storage) to streamline training, artifact management and reproducibility for production-ready ML models. Explains a CI/CD pipeline that retrieves models from storage, builds Docker images and publishes model-serving containers, and demonstrates how this setup automates packaging and serving prior to deployment.
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