Develop model selection frameworks

About this Video

• Video Title: Develop model selection frameworks
• Channel: Gooru Content
• Speakers: None specified in transcript.
• Duration: 00:04:38

Introduction

This video outlines a structured methodology for selecting foundation models for successful generative AI development. It emphasizes the importance of choosing the right model not only for current needs but also for future scalability, considering factors beyond simple performance metrics.

Key Takeaways

• Strategic Model Selection Process: A multi-step process is crucial, beginning with defining precise requirements, scouting potential models, rigorous technical vetting, and assessing non-performance factors (risk, total cost of ownership). The process must be iterative.
• Granular Requirement Definition: Requirements should extend beyond performance metrics, including task type, quality metrics (accuracy, creativity), domain specificity, latency constraints, cost, data sensitivity, and deployment options.
• Rigorous Vetting and Testing: Shortlisted models should be tested against a specific use case using custom test datasets reflecting real-world scenarios. Automated metrics and human-in-the-loop evaluations are recommended. Strategic prompting is key to testing specific model capabilities.
• Total Cost of Ownership (TCO) and Risk Management: A comprehensive TCO analysis is essential, including inference, licensing, infrastructure, fine-tuning, and MLOps costs. Data security, privacy, potential bias, ethical risks, and vendor lock-in must be assessed.
• Iterative Approach: Model selection is an ongoing process, requiring continuous monitoring of model performance and adaptation to evolving needs.