Machine Learning in Production
Completed by Nima Sedghiye
April 10, 2024
11 hours (approximately)
Nima Sedghiye'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
- Category: Data Integrity
- Category: MLOps (Machine Learning Operations)
- Category: Continuous Deployment
- Category: Model Evaluation
- Category: Data Synthesis
- Category: Model Training
- Category: System Monitoring
- Category: Data Preprocessing
- Category: Data Validation
- Category: Applied Machine Learning
- Category: Data Maintenance
- Category: Model Deployment
