- Model Deployment
- Continuous Deployment
- Feature Engineering
- Applied Machine Learning
- Model Evaluation
- Data Validation
- Data Pipelines
- Data Preprocessing
- MLOps (Machine Learning Operations)
- Continuous Monitoring
- Data Quality
- Cloud Deployment
Machine Learning in Production
Completed by Omar Ahmed Desoky
April 16, 2024
11 hours (approximately)
Omar Ahmed Desoky'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
