Machine Learning in Production
Completed by Michael SCHERER
January 21, 2022
11 hours (approximately)
Michael SCHERER'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: Model Deployment
- Category: Continuous Monitoring
- Category: Data Quality
- Category: Unstructured Data
- Category: Data Integrity
- Category: Data Maintenance
- Category: Application Deployment
- Category: Model Evaluation
- Category: MLOps (Machine Learning Operations)
- Category: Data Synthesis
- Category: Continuous Deployment
- Category: Model Optimization
