- Machine Learning
- Model Evaluation
- Data Preprocessing
- Continuous Monitoring
- Debugging
- MLOps (Machine Learning Operations)
- Data Quality
- Feature Engineering
- Model Deployment
- Data Pipelines
- Cloud Deployment
- Data Validation
Machine Learning in Production
Completed by Lazaro Janier Gonzalez-Soler
February 15, 2024
11 hours (approximately)
Lazaro Janier Gonzalez-Soler'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
