Machine Learning in Production
Completed by Heymin Kim
March 25, 2024
11 hours (approximately)
Heymin Kim'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
- Category: Continuous Deployment
- Category: MLOps (Machine Learning Operations)
- Category: Data Maintenance
- Category: Application Deployment
- Category: Model Training
- Category: Unstructured Data
- Category: Data Integrity
- Category: Data Quality
- Category: Applied Machine Learning
- Category: Data Validation
- Category: Data Synthesis
- Category: Model Deployment
