Enterprise Healthcare ML Project — SQL Analytics, XGBoost, FastAPI, MLflow, DVC, Docker, EKS & Governance
Sub Category
- Data Science
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Objectives
- Build an end-to-end AI system from raw data to cloud deployment using real-world architecture
- Design ML pipelines with SQL, feature engineering, and leakage-safe model training
- Use MLflow and DVC for experiment tracking, data versioning, and reproducible pipelines
- Develop production-ready APIs using FastAPI with validation, logging, and model loading
- Implement drift detection using PSI and trigger automated retraining pipelines
- Containerize applications using Docker and deploy scalable services on AWS ECR and EKS
- Connect data, ML, MLOps, APIs, monitoring, and cloud into one cohesive system
- Think like an architect and design production-first AI systems, not just models
Pre Requisites
- Basic understanding of Python programming
- Familiarity with machine learning concepts (classification, features, evaluation metrics)
- Basic knowledge of SQL is helpful but not mandatory
- No prior MLOps or cloud experience required (covered step by step)
- A system capable of running Python, Docker, and basic data processing workloads
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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Coupon Code(s)