ML CI/CD Pipeline
Standard CI/CD doesn't work for ML. You need to test data, not just code. You need to validate model quality, not just unit tests. We build ML-specific CI/CD that handles the unique challenges of shipping models — data validation, training triggers, model evaluation gates, and staged deployment.
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Code CI (The Familiar Part)
Linting, type checking, and unit tests run on every push — same as regular CI. But ML code also gets training smoke tests (does the model train for 1 epoch without crashing?), inference contract tests (does the endpoint accept valid input and return expected output schema?), and data loading tests (do the data loaders handle edge cases?). These catch integration issues before expensive training runs.
Data Validation Pipeline
Data changes trigger their own pipeline: schema validation, distribution checks, outlier detection, and freshness verification. Data PRs — yes, we treat data changes like code changes — get validated automatically. A new training dataset goes through the same rigor as a code change. This is where most ML bugs hide, and most ML CI/CD ignores it.
Training & Evaluation Gates
Merged code or validated data triggers a training run. The pipeline waits for training completion, then evaluates the model against a held-out test set and the currently deployed model. Performance gates check accuracy, latency, model size, and fairness metrics. Only models that pass all gates get promoted to the registry staging stage.
Staged Model Deployment
Promotion from staging to production follows a defined pipeline: shadow deployment (serve both models, compare outputs), canary release (5% traffic to new model), and full rollout. Each stage has automated rollback triggers. The entire flow from code push to production deployment is automated, auditable, and reversible.
Why Anubiz Engineering
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Skip the research. Tell us what you need, and we'll scope it, implement it, and hand it back — fully documented and production-ready.