Introduction
Building effective generative AI applications requires selecting the right foundation model for your specific use case. With thousands of models available, you need a structured approach to discover, compare, deploy, and validate that a model meets your requirements.
Consider a scenario where you're building an AI-powered customer support chatbot for a retail company. You need to select a language model that can understand customer questions, provide accurate responses, and maintain appropriate tone and safety standards. But how do you choose from the vast catalog of available models? How do you know if a model performs well for your specific needs? And once deployed, how do you measure and improve its performance?
The Microsoft Foundry portal provides a comprehensive platform for this entire workflow. You can explore over 1,900 models from providers like Microsoft, Anthropic, OpenAI, Meta, and Hugging Face. You can compare models using industry-standard benchmarks for quality, safety, cost, and performance. After selecting a model, you deploy it to an endpoint where your application can consume it. Finally, you evaluate the model's performance using both automated metrics and manual testing to ensure it meets your quality and safety requirements.
In this module, you explore how to use the Microsoft Foundry portal to select, deploy, and evaluate models from the model catalog. You learn how to make informed decisions about model selection, understand different deployment options, and assess model performance using various evaluation approaches.
By the end of this module, you'll be able to:
- Explore and filter models in the model catalog
- Compare models using benchmark metrics for quality, safety, cost, and performance
- Deploy a model to an endpoint and test it in the playground
- Evaluate model performance using manual and automated approaches
- Understand different evaluation metrics and when to use them