Lilly Tech Systems
Build an MLOps Pipeline
Build a production-ready MLOps pipeline from scratch. Learn data versioning with DVC, experiment tracking with MLflow, automated CI/CD with GitHub Actions, model deployment, and production monitoring — all with full working code.
Project Build Path
Follow these lessons in order to build the complete project step by step, or jump to any section you need.
1. Project Setup
Architecture, MLflow and DVC setup, and project scaffolding.
2. Data Versioning
Track datasets with DVC, remote storage, and pipelines.
3. Experiment Tracking
MLflow experiments, parameters, metrics, and artifacts.
4. Model Registry
MLflow registry, stage transitions, and approval workflow.
5. CI/CD Pipeline
GitHub Actions, automated training, and validation gates.
6. Model Deployment
Docker, FastAPI serving, and health checks.
7. Production Monitoring
Drift detection, performance tracking, and retraining triggers.
8. Enhancements
Multi-model, A/B testing, Kubernetes, and FAQ.
What You Will Build
By the end of this project, you will have a fully functional application that can:
Version Data & Models
Track datasets with DVC and models with MLflow registry for full reproducibility.
Track Experiments
Log parameters, metrics, and artifacts for every training run with MLflow.
Automate CI/CD
Use GitHub Actions to train, validate, and deploy models automatically on code changes.
Deploy & Monitor
Serve models via FastAPI in Docker with drift detection and automatic retraining.