- Model Evaluation
- Feature Engineering
- Model Deployment
- Data Pipelines
- Cloud Deployment
- Data Validation
- Continuous Monitoring
- Applied Machine Learning
- Data Preprocessing
- Machine Learning
- Continuous Deployment
- Data Quality
Machine Learning in Production
Completed by Adrian Guillermo Zuur Pedraza
August 22, 2021
11 hours (approximately)
Adrian Guillermo Zuur Pedraza'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
