- Model Evaluation
- Machine Learning
- Debugging
- MLOps (Machine Learning Operations)
- Cloud Deployment
- Data Validation
- Feature Engineering
- Continuous Deployment
- Continuous Monitoring
- Data Pipelines
- Data Quality
- Model Deployment
Machine Learning in Production
Completed by Damian Berger
May 27, 2024
11 hours (approximately)
Damian Berger'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
