Machine Learning in Production
Completed by Tetiana Vynograd
July 11, 2022
11 hours (approximately)
Tetiana Vynograd'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 Training
- Category: MLOps (Machine Learning Operations)
- Category: Applied Machine Learning
- Category: Continuous Monitoring
- Category: Continuous Deployment
- Category: Data Quality
- Category: Application Deployment
- Category: Machine Learning
- Category: Model Evaluation
- Category: Model Deployment
- Category: Data Validation
- Category: Unstructured Data
