mlops-and-infra

What problem does it solve?

This Skill prevents machine learning projects from becoming unreproducible, untracked, undeployable, and unmonitored by enforcing principal-engineer operational standards from the start.

Core Features & Use Cases

• Experiment Tracking: Logs hyperparameters, dataset versions, git commit hashes, environments, and run metadata before training begins.
• Reproducibility & Packaging: Requires pinned runtimes, immutable data snapshots, deterministic training, and artifacts packaged with preprocessing and schemas.
• Deployment & Monitoring: Defines serving APIs, model registry stages, canary or shadow rollout strategies, drift detection, latency SLOs, and retraining triggers.
• Infrastructure as Code: Enforces cloud resources, secrets handling, and tagging through declarative infrastructure rather than manual console setup.
• Use Case: A team can turn a one-off training script into a fully operational pipeline with tracked experiments, versioned artifacts, automated checks, and production monitoring.

Quick Start

Ask the assistant to apply the mlops-and-infra standards to your ML project so it becomes reproducible, deployable, and monitored end to end.