- Feature Engineering
- Applied Machine Learning
- Continuous Monitoring
- Data Pipelines
- MLOps (Machine Learning Operations)
- Data Quality
- Debugging
- Model Evaluation
- Data Validation
- Cloud Deployment
- Data Preprocessing
- Model Deployment
Machine Learning in Production
Completed by Muhammad Ehtisham Hassan
October 31, 2024
11 hours (approximately)
Muhammad Ehtisham Hassan'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
