Machine Learning Pipeline Template - From Data to Deployment

A machine learning pipeline connects data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment in one reproducible process. This template follows MLOps best practices and includes sections for data versioning, experiment tracking, model registry, deployment configuration, and monitoring. Use it to set up your ML projects in a scalable and maintainable way.

Variations

How to use

Step 1: Download the template and adapt it to your use case. Step 2: Define data sources and how often data is refreshed (batch vs streaming). Step 3: Document feature engineering steps and optionally the feature store structure. Step 4: Describe the training process: framework, hyperparameters, validation strategy, and evaluation metrics. Step 5: Define deployment strategy (batch export, API, edge) and rollback procedures. Step 6: Set up monitoring: data drift, model performance, and SLA alerts. Step 7: Integrate into CI/CD for automated training and deployment on code or data changes.