MLOps-Courses

MLOps-Courses/mlops-coding-skills

Skill Claude CodeCodex

Automate an MLOps project with mise tasks, lefthook hooks, Docker images, GitHub Actions, and MLflow tracking on a SQL backend. Use when adding a task runner, git hooks, CI/CD, or experiment tracking to a working package.

MLOps-Courses/mlops-coding-skills

Skill Claude CodeCodex

Prepare a project for public collaboration — license, code of conduct, docs, branch rulesets, templates, and git-cliff releases. Use when open-sourcing a repository, onboarding contributors, or cutting a tagged release.

MLOps-Courses/mlops-coding-skills

Skill Claude CodeCodex

Convert notebook prototypes into a distributable Python package with a src layout, a domain/io/application split, and validated OmegaConf plus Pydantic configuration. Use when moving code out of notebooks or designing entrypoints.

MLOps-Courses/mlops-coding-skills

Skill Claude CodeCodex

Initialize a production-ready Python MLOps project with uv, git, mise, and a shared editor setup. Use when starting a new repository, writing its first pyproject.toml, or repairing an inconsistent project skeleton.

MLOps-Courses/mlops-coding-skills

Skill Claude CodeCodex

Make an ML system a glass box with reproducible runs, MLflow dataset lineage, drift monitoring, alerting, and SHAP explanations. Use when a deployed model needs traceability, monitoring, alerting, or explanation.

MLOps-Courses/mlops-coding-skills

Skill Claude CodeCodex

Structure reproducible Jupyter notebooks with a fixed section layout, hoisted configuration, and leakage-free scikit-learn pipelines. Use when exploring a dataset, training a first model, or preparing a notebook for promotion.

MLOps-Courses/mlops-coding-skills

Skill Claude CodeCodex

Add the validation layers that gate a merge — ty typing, Ruff linting, pytest coverage, structured logging, and the trivy, pip-audit, and gitleaks scans. Use when hardening code quality or wiring the mise run check task.

MLOps-Courses/agentops-open-course

Skill Claude CodeCodex

Prioritize open incidents deterministically. Use when the engineer asks what to investigate first, requests a queue ranking, or needs an evidence-backed triage summary.

MLOps-Courses/agentops-open-course

Skill Claude CodeCodex

Propose and verify safe, runbook-backed incident remediation. Use when the engineer asks how to fix a known incident or initiate or approve a guarded mock action.

MLOps-Courses/agentops-open-course

Skill Claude CodeCodex

Build offline checks and model-backed evidence for an LLM agent — trajectory scoring, groundedness/citation coverage, a token-cost regression tripwire, and side-by-side prompt A/B. Use when a prompt or model change might silently regress behavior, cost, or grounding, or when "it looked fine" is your only test.

MLOps-Courses/agentops-open-course

Skill Claude CodeCodex

Add layered safety to an LLM agent — PII redaction, prompt-injection spotlighting, human approval on state-changing tools, and a configuration-controlled write kill-switch. Use when an agent can take consequential actions, when tool output could carry injected instructions, or when user data must be redacted before it…

MLOps-Courses/agentops-open-course

Skill Claude CodeCodex

Run the operational loop for an LLM agent that is itself a production workload — detect, triage, mitigate with existing controls, review blamelessly, and convert each incident into a durable regression check. Use when an agent in production burns its error budget, regresses latency or cost, or misbehaves, and you need…

MLOps-Courses/agentops-open-course

Skill Claude CodeCodex

Split an LLM agent into a coordinator plus least-privilege specialists so that a prompt injection cannot reach a tool it was never given, containing blast radius by construction rather than by instruction. Use when one agent holds both untrusted-content readers and consequential write tools, or when you are deciding…

MLOps-Courses/agentops-open-course

Skill Claude CodeCodex

Make an LLM agent's tool and model calls survive flaky and dead dependencies with deadlines, bounded retries, a circuit breaker, and a validated model fallback. Use when an agent hangs on a slow tool, storms a down gateway with retries, or has no failover when its model endpoint is unreachable.

MLOps-Courses/agentops-open-course

Skill Claude CodeCodex

Bound an LLM agent's per-session token spend and attribute cost, so a runaway loop ends with an actionable message instead of an open-ended bill. Use when an agent's multi-step loop can spend without limit, when you need per-session token accounting, or when a delegation chain could multiply model calls.

MLOps-Courses/agentops-open-course

Skill Claude CodeCodex

Index of the AgentOps patterns for operating LLM agents in production — telemetry, guardrails, resilience, evaluation, token budgets, least privilege, and incident response — with a pointer to the full open-source course. Use when you want an overview of how the AgentOps skills fit together, or where to start…

MLOps-Courses/agentops-open-course

Skill Claude CodeCodex

Instrument an LLM agent with OpenTelemetry traces, metrics, and logs so you can see why a turn behaved as it did, with user content kept out of spans by default. Use when an agent is a black box in production, when you need per-turn traces of model and tool calls, or when wiring an agent to MLflow, Prometheus…