Machine Learning in Production
Completed by Sérgio Saraiva de Sousa Neto
April 1, 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
- Category: Continuous Monitoring
- Category: Data Preprocessing
- Category: Continuous Deployment
- Category: MLOps (Machine Learning Operations)
- Category: Model Optimization
- Category: System Monitoring
- Category: Machine Learning
- Category: Model Deployment
- Category: Unstructured Data
- Category: Data Integrity
- Category: Data Synthesis
- Category: Model Training
