Develop model selection frameworks

About this Video

• Video Title: Develop model selection frameworks
• Channel: Gooru Content
• Speakers: None 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 need for a strategic approach to navigate the rapidly expanding landscape of AI models, considering not only technical capabilities but also costs, ethical implications, and future needs. The video details a multi-step process to ensure optimal model selection.

Key Takeaways

• Define Granular Requirements: Go beyond basic performance metrics. Consider task type, desired quality (accuracy, creativity), domain specificity, latency, cost, data sensitivity, and deployment options (cloud, on-premise).
• Scout the Landscape: Identify potential models from various sources (commercial APIs, managed cloud services, open-source). Apply hard constraints early to narrow down options to a short list (3-5).
• Rigorous Vetting: Test shortlisted models against your use case using custom test data reflecting real-world scenarios. Employ automated metrics and human-in-the-loop evaluation for objective and subjective assessment. Proof-of-concept implementation provides valuable insights.
• Comprehensive Risk Assessment: Analyze Total Cost of Ownership (TCO), including inference, licensing, infrastructure, fine-tuning, and MLOps. Assess data security, privacy, potential bias, ethical risks, and vendor lock-in.
• Iterative Process: Model selection is ongoing, not a one-time event. Continuously monitor performance and adapt your approach as models and requirements evolve.