Solution
Machine Learning Systems
Designing, training and operating models that hold up in production.
The problem
A model that performs well in a notebook can fail quietly in production when data shifts or edge cases appear.
Approach
- 1Frame the prediction task and success metric
- 2Build reproducible data and training pipelines
- 3Evaluate against simple baselines
- 4Deploy with monitoring and retraining paths
Typical outputs
- Trained and evaluated models
- Training pipelines
- Deployment setup
- Monitoring dashboards