- Data Quality
- Application Deployment
- Continuous Deployment
- Data Validation
- Software Development Life Cycle
- Continuous Monitoring
- Machine Learning
- MLOps (Machine Learning Operations)
- Applied Machine Learning
- Feature Engineering
- Data Pipelines
Machine Learning in Production
Completed by Radhakrishna Adhikari
February 1, 2024
11 hours (approximately)
Radhakrishna Adhikari'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
