Machine Learning Factory
Machine Learning Factory
From the Idea to the Continuous Maintenance of any Machine Learning Application
About the Book
About the Author
Table of Contents
-
Introduction
- Why is MLE different from Software Engineering
-
Business and Data Understanding
- Defining the Scope of the Machine Learning Application
- Requirements Engineering for Machine Learning
- ML Project Management
- Data Understanding and Collection
- Labs
- References
-
Data Preparation
- Preparing Data for ML
- Clean Data
- Constructing Data
- DataOps for ML
- Labs
- References
-
Modeling
- Research on similar ML problems
- Defining Quality Metrics of ML Model
- Model Selection and Baseline Specification
- Domain Knowledge Incorporation for Model Quality Improvement
- Model Training
- Model Compression
- Ensemble Methods
- Assuring Model Training Reproducibility
- ML Training Pipelines
- Labs
- References
-
Evaluation
- Performance Validation
- Robustness Determination
- Increasing Explainability
- Labs
- References
-
Deployment
- Inference Hardware Specification
- Model Evaluation Under Production Condition
- Versioning for ML Applications
- CI/CD for ML
- Deployment Strategy (Canary Deployment)
- Model Serving Patterns
- Fall-back Plan
- Labs
- References
- Conclusion
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