Machine Learning Factory
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Machine Learning Factory

From the Idea to the Continuous Maintenance of any Machine Learning Application

About the Book

About the Author

Larysa Visengeriyeva
Larysa Visengeriyeva

Larysa has a PhD in Augmented Data Quality at TU Berlin and is a creator of

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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