Evaluating Generative AI Models Using Microsoft Foundry’s Continuous Evaluation Framework

The text explains the importance of continuous evaluation for Generative AI systems, which evolve due to new inputs and model changes. Unlike traditional applications, AI models require ongoing assessment for quality, safety, and efficiency. Microsoft Foundry provides a framework to design and operationalize this, integrating with Azure services. The process involves setting up an evaluation project within Foundry and linking a model endpoint with a test dataset. Users define evaluation metrics, including relevance, safety, and latency. Evaluation pipelines are then run, automatically assessing model responses and generating data. Results are analyzed using a dashboard, providing insights into model performance through key metrics. Continuous evaluation is best incorporated into MLOps pipelines to enable automated evaluation triggered by model updates. Responsible AI and human review are integrated for comprehensive assessment. The text provides a checklist for implementing continuous evaluation. It emphasizes that re-evaluation should also occur when prompts change or usage patterns shift. Ultimately, continuous evaluation is essential for maintaining AI quality. Microsoft Foundry offers an integrated evaluation framework within Azure. Combining automated metrics, human feedback, and responsible AI checks is key.