- Artificial Intelligence and Machine Learning (AI/ML)
- Software Development Life Cycle
- Continuous Monitoring
- Data Quality
- Data Validation
- Data Pipelines
- MLOps (Machine Learning Operations)
- Feature Engineering
- Continuous Deployment
- Debugging
- Applied Machine Learning
- Machine Learning
Machine Learning in Production
Completed by MICHAEL ISAAC OKIRI
December 14, 2021
11 hours (approximately)
MICHAEL ISAAC OKIRI'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
