DevOps Automation Checklist for AI & Machine Learning

Define VPCs, subnets, GPU node pools, and quota requests in Terraform, then version and review them via pull requests. This prevents drift and ensures GPU capacity is available when training ramps up.

Create Docker or Packer images with pinned CUDA, cuDNN, Python, and drivers, then tag them by hash. Immutable images eliminate environment skew that breaks training parity across dev and prod.

Configure Kubernetes node groups with GPU labels and taints, and use Cluster Autoscaler to scale only GPU pools for training or inference workloads. This keeps costs manageable while meeting peak demand.

Use GitHub Actions OIDC or GitLab Workload Identity to assume cloud roles, and store keys in Vault or AWS Secrets Manager. Removing long-lived credentials improves security for CI pipelines and training jobs.

Generate conda-lock or uv lock files and bake them into containers for dev, staging, and prod. Reproducible dependency resolution reduces flaky training runs and deployment failures.

Use spot or preemptible instances where safe, schedule nightly scale-downs, and set budget alerts tied to tags like project and run_id. Cost automation prevents runaway bills during hyperparameter sweeps.

Run Great Expectations or Soda scans on pull requests that modify schemas or transforms, and fail builds on drift. This protects downstream training and serving from breaking changes.

Track dataset snapshots and lineage in code, and tie versions to model runs. Rebuilding a model with the exact data snapshot becomes a single command instead of guesswork.

Use Airflow, Dagster, or Prefect to schedule idempotent, incremental loads with exponential backoff and OpenLineage metadata. This improves reliability and auditability of training data preparation.

Adopt Feast or a managed store, define features as code, and validate offline and online consistency in CI. Automated materialization jobs ensure serving lookups align with training time features.

Track freshness, completeness, and null rates with Evidently, Monte Carlo, or whylogs, and alert on SLO breaches. Automated checks catch silent failures long before model performance drops.

Scan incoming tables with Deequ or Cloud DLP and mask or synthesize sensitive fields in dev and staging. This reduces risk while preserving testability for pipelines and evals.

Wrap trainers with a CLI and Hydra or OmegaConf configs, and persist hyperparameters, metrics, and artifacts to MLflow or Weights & Biases. This makes runs reproducible and easy to compare.

Sync notebooks with Jupytext, execute via Papermill in CI, and fail builds on execution errors. Promotion to production requires code paths that already executed cleanly end to end.

Use Optuna or Ray Tune with population based methods and Kubernetes or Ray autoscaling. Budget-aware search saves cost while exploring enough of the space for measurable gains.

Store content hash based caches in object storage and mount them in training pods. This prevents redundant feature engineering work across runs and speeds up iteration loops.

Promote models through stages in MLflow Model Registry or a similar system that records data version, code commit, and environment. Review gates on stage transitions enforce governance.

Version prompts with lightweight templating, and run evals like RAGAS or DeepEval on every change to prompt, retrieval, or safety rules. Block deploys if toxic or hallucination metrics regress.

Use pytest with small test datasets, plus mypy and ruff for type and style checks. Fast feedback loops keep ML repos healthy without burning GPU minutes.

Validate models with contract tests, golden datasets, and offline versus online parity checks, then rehearse canaries in a staging cluster. This prevents subtle scoring drift in production.

Integrate Trivy or Grype in CI, generate SBOMs with Syft, and sign images with Cosign. Security automation reduces CVE exposure before models hit production.

Use Argo Rollouts or Flagger to canary services based on error rate, latency, and model KPIs. Roll back automatically when thresholds are breached to safeguard user experience.

Ship Helm charts that define idempotent batch runs, exponential backoff, and dead letter queues. Reliable batch retraining and backfills become routine instead of fire drills.

Use Cookiecutter to create opinionated ML repos including GitHub Actions or GitLab CI, code formatters, data validators, and deployment manifests. Consistent scaffolds reduce onboarding time.

Emit spans from data jobs, training, and inference services with consistent attributes like run_id and dataset_hash, then ship to Tempo or Jaeger. Traces make it easy to pinpoint latency and failures.

Export Prometheus metrics for latency, throughput, accuracy, drift, and token usage, and build Grafana dashboards per service. The on-call engineer gets immediate situational awareness.

Schedule Evidently or whylogs jobs that compute PSI and KPI deltas, and trigger alerts or rollbacks on regressions. Early drift detection avoids prolonged quality degradation.

Log prompts, response metrics, safety flags, and token costs to a warehouse and visualize anomalies. This supports A-B tests on prompts and keeps monthly bills under control.

Document known failure modes, add ChatOps commands to flip traffic back, and wire SLO breaches to rollback via Argo Rollouts. Faster recovery reduces MTTR for model incidents.

Tag compute, storage, and APIs by project, team, and run_id, then aggregate costs in dashboards with budget alerts. Per-experiment visibility guides decisions on when to stop costly searches.