Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

In traditional software, CI/CD builds source code and runs unit tests; in machine learning systems, the model often needs to be retrained, re-evaluated and redeployed even when not a single line of code has changed, simply because new data arrived or distributions shifted. This course unites software CI/CD discipline with automated Continuous Training (CT).

Data-schema and behavioural model testing on pull requests, posting automated metric comparisons to PRs with GitHub Actions/GitLab CI, schedule- or drift-triggered retraining workflows (Prefect / Argo Workflows) and GitOps-driven model promotion with ArgoCD are covered hands-on. Participants design and build an end-to-end CI/CD/CT pipeline for their organisation.

Who Should Attend?

  • MLOps, DevOps and platform engineers automating model training and delivery
  • Machine learning engineers maintaining frequently retrained models (recommendations, pricing, forecasting, fraud)
  • Software architects adapting enterprise CI/CD pipelines for AI workloads
  • Prerequisite: Git, Docker, basic CI/CD (GitHub Actions or GitLab CI) and Python proficiency

Key Takeaways

  • Build a three-tier ML Continuous Integration (CI) pipeline validating code, data schemas and model artefacts
  • Dispatch cloud/GPU runners from pull requests and publish automated champion-vs-challenger evaluation comments
  • Orchestrate Continuous Training (CT) pipelines triggered by schedules, fresh data arrival or drift alerts
  • Implement automated champion/challenger quality gates that block promotion when a retrained model underperforms
  • Manage model version rollouts declaratively and reversibly using GitOps and ArgoCD

Curriculum

From Classical CI/CD to CI/CD/CT for Machine Learning
  • Managing change across the three axes of code, data and models, and MLOps maturity levels
  • Separating fast PR smoke checks from long-running full retraining jobs across CI and CT
Continuous Integration (CI) and the ML Testing Pyramid
  • Unit-testing feature transforms, 1-epoch tiny-batch overfit smoke tests and determinism checks
  • Behavioural model tests (invariance, directional expectations), critical-slice gates and latency assertions
PR-Driven Model Reviews and GPU Runner Orchestration
  • Dispatching self-hosted GPU runners in GitHub Actions / GitLab CI and caching heavy ML dependencies
  • Posting automated metric tables, confusion matrices and champion diffs directly onto pull requests
Continuous Training (CT) Workflow Orchestration
  • Retraining triggers in Prefect, Airflow or Argo Workflows: cron schedules, data-volume thresholds and drift webhooks
  • Automated data validation, training, Champion-vs-Challenger evaluation and conditional Registry promotion
Continuous Delivery (CD) and GitOps Model Rollouts
  • Updating deployment manifests upon @champion promotion and syncing to Kubernetes via ArgoCD
  • Post-deployment health probes, automated rollback on metric degradation and complete audit trails

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