Building an End-to-End ML Deployment Pipeline with MLflow, FastAPI, and Docker

Deploying machine learning models is more than just training — it’s about tracking, versioning, serving, and monitoring. In this post, I’ll walk you through how I built a production-ready ML pipeline using:

• MLflow for experiment tracking and model registry
• FastAPI for serving models via REST API
• MinIO for artifact storage (S3-compatible)
• Docker Compose for orchestration

👉 Full source code:
🔗 github.com/liviaerxin/mlops-fastapi-mlflow-minio

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Project Overview

This project provides:

• A structured pipeline to log, register, and serve ML models
• Docker-based setup with MLflow, FastAPI, and MinIO
• Simple training and inference workflows

📁 Project structure:

.
├── docker-compose.yml
├── inference-server/
├── mlflow-local-train-remote-register/
├── mlflow-server/
├── train.py
└── README.md

🗂️ For full instructions, check the README

Key Components

MLflow

MLflow is a platform to manage the ML lifecycle, including experimentation, reproducibility, and deployment.

• Experiment tracking (mlflow.log_param, mlflow.log_metric)
• Model registration and versioning
• Stage promotion (Staging, Production)

NOTE: MLflow is used remotely in docker container environment here.

MinIO

S3-compatible artifact store used by MLflow to save models. For production, you might migrate to use AWS S3 or GCS.

NOTE: MinIO is used remotely in docker container environment here.

FastAPI

Simple REST API to serve ML models loaded from the MLflow registry.

Loads the remote model via:

mlflow.set_tracking_uri(os.environ.get("MLFLOW_SERVER", "http://127.0.0.1:5000"))
model = mlflow.pyfunc.load_model(f"models:/{MODEL_NAME}/{MODEL_STAGE}")

NOTE: FastAPI is used remotely in docker container environment here.

Training and Registering a Model

NOTE: mlflow-local-train-remote-register is used locally outside the docker container environment here.

Run the training script:

python train.py

Then log and register it to a remote MLflow server:

python register-remote.py

Key code snippet:

os.environ["MLFLOW_S3_ENDPOINT_URL"] = "http://127.0.0.1:9000" # Expose the minio via host 9000 port
mlflow.set_tracking_uri("http://127.0.0.1:5001") # Expose the mlflow server via host 5001 port
mi = mlflow.pytorch.log_model(model, artifact_path="model", registered_model_name=MODEL_NAME)

Bonus: Updating Models and Rolling Deployment

You can update models by:

• Logging a new version to MLflow
• Promoting it to Production stage
• Using FastAPI logic to reload the latest version without downtime

You may also support blue-green deployments using Docker or Kubernetes.

Conclusion

This setup gives you a scalable and reproducible ML deployment pipeline with clear separation of concerns:

• MLflow handles tracking and registry
• MinIO manages artifact storage
• FastAPI exposes inference endpoints
• Docker Compose glues it all together

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🔗 GitHub Repo

All code and instructions: 🔗 github.com/liviaerxin/mlops-fastapi-mlflow-minio