- Machine Learning
- Model Evaluation
- Continuous Monitoring
- Continuous Deployment
- Data Quality
- Applied Machine Learning
- MLOps (Machine Learning Operations)
- Model Deployment
- Data Pipelines
- Data Validation
- Cloud Deployment
- Feature Engineering
Machine Learning in Production
Completed by Harris Naeem
July 29, 2022
11 hours (approximately)
Harris Naeem'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
