- Data Quality
- Applied Machine Learning
- Continuous Monitoring
- MLOps (Machine Learning Operations)
- Continuous Deployment
- Machine Learning
- Data Pipelines
- Feature Engineering
- Software Development Life Cycle
- Application Deployment
- Data Validation
- Data-Driven Decision-Making
Machine Learning in Production
Completed by SIDDHARTH SHANKAR BHANDARI
December 25, 2021
11 hours (approximately)
SIDDHARTH SHANKAR BHANDARI'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
