Python Machine Learning Pipeline
Create an end-to-end ML pipeline with data processing, model training, and deployment.
Prompt (feel free to adjust it):
Develop a complete machine learning pipeline using Python with pandas for data preprocessing, scikit-learn and TensorFlow for model development, MLflow for experiment tracking, Apache Airflow for workflow orchestration, Docker for containerization, and FastAPI for model serving. Include data validation with Great Expectations, automated retraining, A/B testing framework, monitoring dashboards, and deployment on AWS/GCP with proper CI/CD practices.
Use Cases
- Predictive analytics systems
- Recommendation engines
- Automated decision making
- Data science productionization