ML Pipeline Best Practices
A practical guide to building reproducible, maintainable machine learning pipelines from data ingestion through model deployment.
Pipeline Architecture
Every ML pipeline we build follows a clear five-stage architecture. Each stage is independently testable, version-controlled, and observable.
Pull raw data from APIs, databases, or file systems into a staging area. Use Apache Airflow or n8n for scheduling.
Handle missing values, outliers, type coercion, and deduplication. Output validated datasets with quality metrics.
Transform raw data into model-ready features. Use feature stores (Feast) for reusability across models.
Train models with experiment tracking (MLflow). Log hyperparameters, metrics, and artifacts for reproducibility.
Serve models via REST API (FastAPI) or batch prediction. Implement A/B testing and canary deployments.
Key Principles
Reproducibility
Pin all dependencies, version datasets, and log every experiment. You should be able to recreate any model from any point in time.
Monitoring
Track data drift, model performance, and prediction latency in production. Alert on degradation before it impacts users.
Testing
Unit test data transformations, integration test the pipeline end-to-end, and validate model outputs against a golden dataset.
Automation
Automate retraining triggers based on performance thresholds or scheduled intervals. Humans review before promotion.
Recommended Tooling
Apache Airflow for complex DAGs, n8n for simpler webhook-driven pipelines
MLflow for local/self-hosted, Weights & Biases for managed
Feast for open-source, Tecton for enterprise-grade feature serving
FastAPI + Docker for REST endpoints, BentoML for batched inference
Great Expectations for data quality checks at each pipeline stage
Need help building an ML pipeline? Get in touch and we will design the right architecture for your data and use case.