Most machine learning courses focus on building models in isolation. You train a model, evaluate accuracy, and consider the job done. But in real-world systems, that is only a small part of the problem.
Organizations do not need models. They need systems that can:
- ingest and process real-world data
- generate reliable predictions
- serve those predictions through APIs
- monitor performance over time
- adapt when data changes
This course is designed to bridge that gap. The Story Behind This Capstone. Imagine a large hospital network handling thousands of patients every day. Patients arrive with different conditions. Some cases are routine, while others escalate into high-risk situations requiring immediate attention. At the same time, every visit generates billing records, which are later submitted to insurance providers. Some claims are approved quickly, while others are delayed or rejected, leading to revenue loss and operational inefficiencies.
