Manual Retraining

Manual retraining is a process in machine learning and AI development where human intervention is required to update or refine a model based on new data, feedback, or performance issues. It involves steps such as data collection, preprocessing, model adjustment, and validation, often performed iteratively to improve accuracy or adapt to changing conditions. This approach contrasts with automated retraining systems that trigger updates without direct human oversight.

Also known as: Human-in-the-loop retraining, Manual model updating, Hands-on retraining, Supervised retraining, Iterative manual refinement

Why learn Manual Retraining?

Developers should use manual retraining when working with critical or sensitive models where precision, interpretability, and control are paramount, such as in healthcare diagnostics, financial fraud detection, or legal applications. It is also essential during initial model development phases, for debugging performance issues, or when dealing with small, non-streaming datasets that require careful curation. This method allows for thorough validation and compliance with regulatory standards, reducing risks of automated errors.

Learning Resources

• book: Machine Learning Yearning by Andrew Ng
• docs: Scikit-learn Documentation: Model Evaluation
• course: Coursera: Machine Learning Specialization
• tutorial: Towards Data Science: Manual vs. Automated Retraining
• video: YouTube: Human-in-the-Loop Machine Learning