- Machine Learning
- Model Evaluation
- Continuous Monitoring
- Continuous Deployment
- Data Quality
- Applied Machine Learning
- MLOps (Machine Learning Operations)
- Model Deployment
- Data Pipelines
- Data Validation
- Cloud Deployment
- Feature Engineering
Machine Learning in Production
Completed by Anahit Khachatryan
November 24, 2024
11 hours (approximately)
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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
