Machine Learning in Production
Completed by Nathanael Andrew Travis
May 10, 2024
11 hours (approximately)
Nathanael Andrew Travis'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: Data Maintenance
- Category: MLOps (Machine Learning Operations)
- Category: Data Integrity
- Category: Unstructured Data
- Category: Machine Learning
- Category: Model Training
- Category: Data Quality
- Category: Model Optimization
- Category: Application Deployment
- Category: Data Synthesis
- Category: Applied Machine Learning
