A Practical Guide to MLOps: Building a Modern Machine Learning Operations Pipeline

• 4/1/2023
• One-minute read

1. Data Management: Version training datasets, track feature lineage, and automate validation (schema drift, null checks).
2. Experimentation: Log parameters, metrics, and artifacts; ensure runs are reproducible (Docker images, environment manifests).
3. Deployment: Package models with API contracts, automate promotion via CI/CD, and support blue/green or canary releases.
4. Monitoring: Track prediction quality (drift, bias, accuracy) and infrastructure metrics; define rollback triggers.

Tooling Options

Choose a minimal set that integrates with your existing CI/CD and data platform rather than adopting everything at once.

Implementation Roadmap

• Phase 1: Standardise notebooks → container images, introduce experiment tracking, and store artifacts in a shared registry.
• Phase 2: Build automated pipelines for training/evaluation, include approval gates, and manage infrastructure as code.
• Phase 3: Add continuous monitoring, automated retraining triggers, and incident response playbooks for model failures.

Governance Considerations

• Document model cards, data sources, and intended use cases for compliance.
• In regulated industries, align with Responsible AI policies and obtain sign-off from risk/legal before production deployment.
• Secure secrets and credentials; restrict production access to approved service accounts.

Further Reading

• MLOps Maturity Model (Microsoft)
• MLflow Documentation