- MLOps (Machine Learning Operations)
- Application Deployment
- Machine Learning
- Applied Machine Learning
- Feature Engineering
- Software Development Life Cycle
- Continuous Monitoring
- Data Quality
- Data Pipelines
- Continuous Deployment
- Data Validation
Machine Learning in Production
Completed by Yun Jung Cheng
April 7, 2024
11 hours (approximately)
Yun Jung Cheng'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
