- Data Preprocessing
- Data Validation
- Continuous Deployment
- Machine Learning
- MLOps (Machine Learning Operations)
- Applied Machine Learning
- Model Evaluation
- Continuous Monitoring
- Cloud Deployment
- Feature Engineering
- Debugging
- Data Quality
Machine Learning in Production
Completed by Ajinkya Rajiv Mishrikotkar
August 8, 2022
11 hours (approximately)
Ajinkya Rajiv Mishrikotkar'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
