MLOps-Model-Development-to-Production-deployment

🧱Model Development

• Processed NYC taxi trip data with engineered features like pickup/dropoff zones and trip distance.
• Tuned XGBoost and Random Forest models using Hyperopt, improving RMSE by ~30%.
• Tracked all experiments, metrics, and artifacts using MLflow.

🧱 Pipeline Orchestration

• Designed a modular ML pipeline using Apache Airflow to automate preprocessing and training stages.
• Configured Docker + CeleryExecutor for reproducible, distributed task execution.
• Used XCom for smooth data passing and monitoring through the DAG.

🧱 Experiment Management

• Logged models to MLflow Model Registry, versioned them, and promoted top models to "Production" stage.
• Enabled consistent experiment comparison and artifact retrieval.

🧱 Deployment

• Deployed the trained model as a REST API using Flask, allowing real-time predictions via user input.
• Containerized the app with Docker to simulate a real-world, always-on service.

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Implementation details

Docker-only

# build once
docker build -t ride-duration-prediction-service:v1 .

# run anywhere, no local installs
docker run -it --rm -p 9696:9696  ride-duration-prediction-service:v1

I have all my models saved in a SQLite Db on MLFlow. You can find my run models (XGBoost) in this folder:

Experiment Tracking using MLFlow/mlruns/1

You can serve a specific model run directly using the run ID. Example:

Replace <run_id> with any of your run folder names (e.g., 03825066a5284d1981cb73e97e7e0fd4).

mlflow models serve -m ./mlruns/1/<run_id>/artifacts/models_mlflow --port 9696