- Debugging
- Model Evaluation
- Data Pipelines
- Cloud Deployment
- Continuous Deployment
- MLOps (Machine Learning Operations)
- Model Deployment
- Feature Engineering
- Data Quality
- Applied Machine Learning
- Machine Learning
- Continuous Monitoring
Machine Learning in Production
Completed by Shreyas Rajendra Chim
July 14, 2021
11 hours (approximately)
Shreyas Rajendra Chim'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
