Machine Learning in Production
Completed by Yu-Hsien Huang
October 27, 2024
11 hours (approximately)
Yu-Hsien Huang'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 Preprocessing
- Category: Application Deployment
- Category: Continuous Deployment
- Category: MLOps (Machine Learning Operations)
- Category: Data Maintenance
- Category: Model Deployment
- Category: Data Validation
- Category: Continuous Monitoring
- Category: Model Evaluation
- Category: Data Collection
- Category: Applied Machine Learning
- Category: Data Synthesis
