DevOps Automation Checklist for AI & Machine Learning

Tie datasets, labels, and model binaries to git commits so any training run can be recreated exactly. Use DVC remotes on S3 or GCS and enable data locking to prevent silent overwrites.

Define column types, ranges, and null policies that block training when inputs drift. Store expectation suites in the repo and run them in CI and orchestration before feature engineering steps.

Log hyperparameters, metrics, artifacts, and environment hashes for every run. Configure automatic run IDs from CI to correlate pull requests with offline experiments and production deployments.

Build images from NVIDIA CUDA images, then lock Python deps with conda-lock or pip-tools to ensure byte identical builds. Record CUDNN versions, seed values, and compiler flags in the experiment metadata.

Store checkpoints, feature files, and evaluation reports in S3, GCS, or MinIO with bucket versioning and lifecycle transitions to cold storage. Tag artifacts by experiment and environment for automated cleanup.

Convert analysis notebooks into parameterized pipelines, then enforce code quality and style with nbQA checks in CI. Persist executed notebooks as artifacts for audit and documentation.

Run unit tests and a tiny training job on a 1 to 5 percent sample to validate data contracts, feature code, and model interfaces. Gate heavy jobs behind these fast checks to reduce cycle time.

Use GitHub Actions, GitLab CI, or Jenkins caches for pip and conda, and cache DVC pull results across jobs. Key caches by lockfile hash to keep cache hits deterministic and safe.

Bake CUDA drivers, cuDNN, and framework wheels into immutable images, then scan for CVEs on every build. Fail the pipeline on critical vulnerabilities or policy violations before pushing to the registry.

Create tests for data loaders, feature generators, and training loops with fixed seeds and deterministic backends where possible. Apply per test timeouts and resource limits to prevent runaway jobs in CI.

Export models to MLflow Model Registry or BentoML bundles only when metric thresholds pass. Include environment YAML, inference signatures, and pre or post processing code in the publish step.

Block merges if validation metrics regress beyond a defined tolerance by querying MLflow or Weights & Biases for the current baseline. Persist comparison reports as build artifacts for review.

Automate EKS, GKE, or AKS clusters with GPU node groups, labels, taints, and the NVIDIA device plugin. Version control AMIs or images preloaded with drivers and verify with a post provision smoke workload.

Template training jobs, batch pipelines, and model servers with charts or overlays that vary resources, secrets, and endpoints by environment. Validate manifests with kubeval or kubeconform in CI.

Store API keys, data credentials, and encryption keys in Vault, AWS Secrets Manager, GCP Secret Manager, or Azure Key Vault. Mount secrets at runtime via CSI drivers and rotate them on a schedule.

Restrict object storage to VPC endpoints, require TLS in transit, and enforce least privilege IAM for training and serving service accounts. Add default encryption with customer managed keys for datasets and artifacts.

Configure cluster autoscaler or Karpenter with GPU instance preferences and max node caps. Set cost budgets and alerts, then schedule off hourly scale downs for non production clusters to avoid idle burn.

Sync manifests from git to clusters and use pull requests as the change control surface. Rollbacks become git reversions, and drift detection alerts when teams bypass desired state.

Build task graphs that capture dependencies across extraction, transformation, and feature generation with exponential backoff and safe re runs. Use unique run IDs or stable keys to prevent duplicate writes.

Run Great Expectations or Soda checks as blocking tasks in your scheduler and fail the pipeline on schema or statistical drift. Route failures to messaging and incident tooling for immediate triage.

Use Kafka with Flink or Spark Structured Streaming to compute online features, then materialize to Redis or DynamoDB for low latency serving. Implement watermarking and exactly once write semantics where possible.

Validate and apply Feast registry diffs in CI, then run a dry run to confirm sources and stores are reachable. Block deploys if offline and online feature definitions diverge or break backward compatibility.

Emit lineage events from Airflow, Spark, or dbt into Marquez so you can trace a model’s inputs all the way back to raw tables. Use lineage to scope blast radius during incidents and rollbacks.

Trigger backfill jobs when source tables or schemas change and write snapshots with timestamped partitions. Keep backfills idempotent and rebuild downstream features only for affected partitions to save compute.

Use distributed trials across GPU nodes with resource limits to avoid cluster starvation. Persist trial artifacts and metrics automatically, then checkpoint best configurations to the model registry.

Centralize all toggles in versioned YAML and bind seeds for data loaders, NumPy, and framework RNGs. Enable deterministic CUDA kernels where supported and serialize the full config with the model artifact.

Calculate per segment metrics and calibration curves using scikit-learn or torchmetrics, then store results as structured artifacts. Fail the build if critical slices regress even when overall metrics improve.

Add Fairlearn or AIF360 checks to CI and require parity thresholds across protected attributes. Export fairness reports alongside model cards so reviewers can approve consciously before production.

Template model documentation from MLflow or W&B runs, data provenance, and evaluation outputs. Publish as HTML or Markdown artifacts and version them with the model for traceable governance.

Produce SBOMs with Syft for images and Python wheels, then sign containers and model bundles with Cosign. Verify signatures in CI and admission controllers before allowing deployments to production clusters.

Roll out models with KServe or Seldon Core integrated with Argo Rollouts to send a small percentage or shadow traffic to the new version. Promote automatically when live metrics stay within safe bounds.

Configure HPA or KEDA to scale on p95 latency, queue depth, or NVIDIA DCGM metrics. Set conservative requests and limits to avoid GPU fragmentation and reserve headroom for burst traffic.

Expose Prometheus metrics for throughput, latency, and error rates, then add OpenTelemetry spans across pre, post processing and model calls. Visualize with Grafana and use exemplars to link traces to spikes.

Ship stdout and stderr to Loki or ELK via Fluent Bit, applying processors to hash or drop sensitive fields. Set index or chunk retention aligned to compliance and export audit logs to cold storage.

Run Evidently to monitor input distributions, target leakage, and performance decay, then alert when thresholds breach. Feed reference windows from recent training data for fair comparisons.

Document runbooks that roll back via GitOps and clear cache or feature toggles, then route alerts to PagerDuty or Opsgenie with escalation policies. Include synthetic checks that verify recovery after rollback.