mlops-and-infra

ML pipelines become reproducible by enforcing dirty-tree checks, immutable data and code versioning, pinned runtimes, and containerized environments before training begins. This ensures every experiment is tracked, packaged with explicit schemas, and ready for automated deployment.

Track ML experiments by logging hyperparameters, dataset versions, git commit hashes, environments, and run metadata before training starts. Capturing this metadata systematically prevents untracked runs and ensures full reproducibility across your team.

Set up model serving CI/CD by defining deployment stages, automated testing, and declarative infrastructure rather than manual console setup. This enforces tagged cloud resources, proper secrets handling, and controlled canary or shadow rollout strategies.

Yes, production monitoring includes drift detection, latency SLOs, and retraining triggers. Defining serving APIs and model registry stages ensures deployed models are continuously monitored for performance degradation and data drift.

Yes, containerized runtimes and explicit schemas are required. Packaging artifacts with preprocessing steps and schemas ensures deterministic training and reliable model serving across different environments and deployment stages.

Infrastructure as code manages cloud resources and secrets by enforcing declarative infrastructure setup with tagged resources. This replaces manual console configuration, ensuring reproducible, secure, and auditable ML infrastructure across training and serving stacks.