What you’ll learn:
- Build scalable MLOps pipelines with Git, Docker, and CI/CD integration.
- Implement MLFlow and DVC for model versioning and experiment tracking.
- Deploy end-to-end ML models with AWS SageMaker and Huggingface.
- Automate ETL pipelines and ML workflows using Apache Airflow and Astro.
- Monitor ML systems using Grafana and PostgreSQL for real-time insights.
