Introduction
Welcome back to our Generative AI series! In the previous article, we discussed the importance of foundation models and the challenges involved in selecting the right one. This article will provide a framework for selecting the right model and explore how public cloud platforms can help with generative AI.
AI Model Selection Framework
To navigate these challenges and select the right model, follow this five-step cyclical approach, keeping governance at its core:
1. Identify a Clear Use Case
Clearly articulate the specific use case, such as generating personalized responses for customer inquiries or predicting equipment failures in a manufacturing process. Craft specific prompts and ideal answers, then work backward to identify the necessary data. Detailed articulation ensures the AI model is trained and tuned for the specific tasks it needs to perform.
- Define Specific Objectives: Clearly define what you want to achieve with AI, like automating customer support to reduce response times and improve customer satisfaction. This ensures the AI implementation directly addresses business goals and delivers measurable outcomes.
- Stakeholder Involvement: Involve key stakeholders as early as possible from different departments to ensure the AI solution meets cross-functional requirements. Engaging stakeholders ensures a comprehensive solution that considers various needs and constraints.
- Use Case Breakdown: Break down the use case into smaller components like natural language understanding and response generation. This helps in understanding specific needs and challenges, making implementation more focused and effective.
2. List All Model Options
Identify various models suitable for the use case, evaluating each model’s size, performance, and risks. Compare general-purpose models with specialized models.
- Comprehensive Research: Research available models thoroughly, including open-source, proprietary, and third-party solutions. Thorough research helps in understanding strengths and limitations, ensuring an informed decision.
- Model Characteristics: Document the characteristics of each model, such as training data size and architecture. Knowing these characteristics helps in assessing model suitability.
- Performance Benchmarks: Look for benchmark performance metrics to compare models objectively, like perplexity scores for text generation quality. Performance benchmarks provide objective criteria for comparison.
3. Identify Key Characteristics
Compare models based on their speed, accuracy, and potential risks. A larger model may offer higher accuracy but at a slower speed and higher cost.
- Model Size vs. Use Case Needs: Match the model size with your use case requirements. A smaller, specialized model might be suitable for applications where response speed is critical.
- Performance Metrics: Use relevant metrics like accuracy, F1 score, recall, and precision. These ensure the model performs well in specific tasks.
- Risk Assessment: Assess risks including data privacy, potential biases, ethical considerations, and regulatory compliance. This ensures the AI model operates within legal and ethical boundaries.
4. Evaluate Model Characteristics for Your Use Case
Assess model performance using metrics such as perplexity or BLEU score, and refine the selection based on cost and deployment needs:
- Pilot Testing: Conduct pilot tests with selected models using a subset of your data. This validates the model’s performance in real-world scenarios before full deployment.
- Performance Evaluation: Evaluate the performance using specific metrics like BLEU and ROUGE and real-world scenarios. This ensures the model can handle practical challenges effectively.
- Iterative Refinement: Employ iterative testing and refinement, incorporating methods like prompt engineering and retrieval-augmented generation (RAG), to enhance model performance. This approach boosts both accuracy and efficiency. Fine-tuning, due to its cost, time requirements, and complexity, should be considered a last resort.
5. Choose the Model that Provides the Most Value
Based on test results, select the model that delivers the best balance of performance, accuracy, and cost-effectiveness. Ensure that the model aligns with the following points:
- Long-term Viability: Consider the long-term viability of the model, including support, updates, and community or vendor support. This avoids future disruptions and ensures sustained performance.
- Integration with Existing Systems: Evaluate how well the model integrates with your existing systems, infrastructure and workflows. Seamless integration reduces implementation time and costs.
- Continuous Monitoring and Evaluation: Implement continuous monitoring and evaluation mechanisms to track model performance over time and make adjustments as needed. This ensures the model remains effective and up-to-date.
Key Evaluation Factors
When evaluating models, consider these crucial factors:
- Accuracy: How closely the generated output matches the desired result. Use relevant metrics like BLEU for translation tasks.
- Reliability: Consistency, explainability, trustworthiness, and avoidance of harmful content. Reliable models build trust through transparency.
- Speed: The response time to user prompts. Balancing speed and accuracy is vital; larger models may be slower but more accurate, whereas smaller models might offer quicker responses with acceptable accuracy.
Adopting a Multi-Model Approach
Organizations often have multiple use cases requiring different models. This multimodel approach ensures each task uses the most suitable foundation model, optimizing performance across diverse applications.
Conclusion
Following a structured framework for selecting the right AI model is crucial for aligning your choices with specific use cases. The next article in the Generative AI series will cover the AI stacks of the top three cloud providers—AWS, Google Cloud, and Microsoft Azure—and how they can support AI initiatives. By partnering with PCG, you can leverage their expertise in AI and machine learning to choose the best foundation models. Stay tuned to learn more about the tools and services available to operationalize AI models effectively.