- Cloud Deployment
- Data Validation
- Data Pipelines
- Model Deployment
- Continuous Monitoring
- MLOps (Machine Learning Operations)
- Applied Machine Learning
- Continuous Deployment
- Machine Learning
- Data Quality
- Feature Engineering
- Data Preprocessing
Machine Learning in Production
Completed by Farzaneh Labbaf
February 28, 2024
11 hours (approximately)
Farzaneh Labbaf'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
