Machine Learning in Production
Completed by Curtis James Goodwin
February 6, 2022
11 hours (approximately)
Curtis James Goodwin'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 Integrity
- Category: Applied Machine Learning
- Category: Application Deployment
- Category: Data Synthesis
- Category: Continuous Deployment
- Category: Model Evaluation
- Category: Data Validation
- Category: Model Optimization
- Category: MLOps (Machine Learning Operations)
- Category: Model Training
- Category: Unstructured Data
- Category: Data Preprocessing
