Machine Learning in Production
Completed by Nami Kim
December 4, 2022
11 hours (approximately)
Nami 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: Data Quality
- Category: Continuous Monitoring
- Category: Model Evaluation
- Category: Data Validation
- Category: Model Training
- Category: Model Deployment
- Category: MLOps (Machine Learning Operations)
- Category: Data Maintenance
- Category: Continuous Deployment
- Category: Data Synthesis
- Category: Applied Machine Learning
- Category: Unstructured Data
