Model Optimization Strategies
Mircea discusses the balance between model accuracy and practical application, emphasizing the importance of confidence scores over sheer performance metrics. He explains that while automl techniques are implicitly used, the focus is on delivering a model that effectively fine-tunes to customer data. The trade-off between overall performance and reliability is crucial, as clients prioritize models that indicate when human intervention is necessary.In this clip
From this podcast
Gradient Dissent - A Machine Learning Podcast
Mircea Neagovici — Robotic Process Automation (RPA) and ML
Related Questions
What are some techniques for training machine learning models?
Can you explain the differences between fine-tuning and training models, specifically LLMs vs custom model applications, and when to use each in the context of the episodes Treating Prompt Engineering More Like Code // Maxime Beauchemin // MLOps Podcast #167 and Fine-tuning Models as well as the episode Navigating Machine Learning Careers: Insights from Meta to Consulting // Ilya Reznik // #286 and the clip Fine Tuning Insights?