Developing model selection frameworks

About this Video

• Video Title: Developing model selection frameworks
• Channel: Gooru Content
• Speakers: None specified in transcript
• Duration: 00:04:22

Introduction

This video outlines a systematic, data-driven approach to selecting foundation models for generative AI applications. It emphasizes moving beyond guesswork to a structured framework that considers both technical and non-technical factors for optimal performance and responsible deployment.

Key Takeaways

• Defining Clear Requirements: Begin with a precise definition of functional (task performance, metrics) and non-functional (latency, cost, security, ethics) requirements.
• Analyzing the Model Landscape: Understand the diverse range of models (proprietary APIs, cloud services, open-source) and their key differentiators (context window, performance benchmarks, cost).
• Rigorous Assessment: Use representative test sets, automated metrics, latency/throughput tests, and expert/user reviews for objective comparisons. Consider the impact of prompting strategies.
• Beyond Technical Benchmarks: Critically evaluate models based on bias, fairness, security, explainability, regulatory compliance, vendor support, and cost analysis.
• Iterative Process: Model selection is an ongoing process requiring continuous evaluation and refinement.