Model Optimization in Microsoft Foundry: Deployment and Evaluations

Validate and launch your customized models seamlessly using Microsoft Foundry! In this installment, we tackle what happens after the training is complete. You'll learn how to deploy fine-tuned models effectively, manage long-term inference costs, and rigorously evaluate your model's performance post-training using a custom grader logic to ensure production-grade reliability. 00:03 Welcome and scenario 00:52 Post-training evaluation 01:20 Demo: Using a custom grader for evaluations 03:35 Cost management 05:50 Model deployment Microsoft Foundry - https://aka.ms/foundry-ft Foundry Finetuning Demos on GitHub - https://aka.ms/ft-demos Azure OpenAI Fintuning Costs: https://aka.ms/aoai-ft-cost Bethany Jepchumba, Twitter/X - https://twitter.com/bethanyjep Bethany Jepchumba, LinkedIn - https://www.linkedin.com/in/bethany-jep/ Bethany Jepchumba, GitHub - https://github.com/bethanyjep