Develop model selection frameworks

About this Video

• Video Title: Develop model selection frameworks
• Channel: Gooru Content
• Speakers: Not specified in the transcript.
• Duration: 00:04:38

Introduction

This video outlines a structured methodology for selecting foundation models for generative AI development. It emphasizes the importance of a multi-step process that considers not only technical performance but also non-functional requirements like cost, risk, and ethical implications to ensure the chosen model is optimal for both present and future needs.

Key Takeaways

• Defining Granular Requirements: The selection process begins with defining precise requirements, including task type, quality metrics (accuracy, creativity), domain specificity, latency constraints, cost, data sensitivity, and deployment options (cloud, on-premise).
• Rigorous Vetting: Shortlisted models (3-5) undergo rigorous testing against real-world scenarios using custom datasets. This involves automated metrics and human-in-the-loop evaluation for subjective quality assessment. Strategic prompting is crucial for testing specific model capabilities.
• Total Cost of Ownership (TCO) and Risk Assessment: The process includes a comprehensive analysis of TCO (inference, licensing, infrastructure, fine-tuning, MLOps) and risk assessment focusing on data security, privacy, potential bias, ethical concerns, and vendor lock-in. The final decision balances these factors with performance needs.
• Iterative Process: Model selection is an ongoing process, not a one-time event. Continuous monitoring of model performance and adaptation to evolving requirements are vital.
• Importance of Prompt Engineering: Designing precise prompts is essential for effectively testing the capabilities of shortlisted models, ensuring they meet the specific needs of the application.