- Model Deployment
- Debugging
- Continuous Deployment
- Model Evaluation
- Continuous Monitoring
- Applied Machine Learning
- Feature Engineering
- Machine Learning
- Data Pipelines
- Data Validation
- Cloud Deployment
- Data Quality
Machine Learning in Production
Completed by Gregory Moro Puppi Wanderley
September 26, 2024
11 hours (approximately)
Gregory Moro Puppi Wanderley's account is verified. Coursera certifies their successful completion of Machine Learning in Production
What you will learn
Identify key components of the ML project lifecycle, pipeline & select the best deployment & monitoring patterns for different production scenarios.
Optimize model performance and metrics by prioritizing disproportionately important examples that represent key slices of a dataset.
Solve production challenges regarding structured, unstructured, small, and big data, how label consistency is essential, and how you can improve it.
Skills you will gain
