Model Mondays is a weekly series to build your Azure AI Foundry Model IQ. In this episode, we focus on fine-tuning and distillation—two essential techniques for customizing and optimizing AI models for your unique application needs. Read on for my recap!
This post was generated with AI help and human revision & review. To learn more about our motivation and workflows, please refer to this document in our Model Mondays website.
About Model Mondays
Model Mondays is a weekly series designed to help you build your Azure AI Foundry Model IQ, one week at a time. Here’s what to expect:
- 5-Minute Highlights – Quick updates on Azure AI models and tools (Mondays)
- 15-Minute Spotlight – A deeper look at a key model, protocol, or feature (Mondays)
- 30-Minute AMA – Friday Q&A with experts from Monday’s episode
Whether you’re just starting out or already working with models, this series is your chance to grow with the community.
Quick links to explore:
Spotlight Topic: Fine Tuning & Distillation
What is this topic and why is it important?
Fine-tuning allows you to adapt a general-purpose, pre-trained model to your specific data or task—boosting accuracy and relevance. Distillation helps you take a large, high-performing model and extract its knowledge into a smaller model. This means you can run AI on smaller devices or scale at lower cost without losing performance.
Together, these techniques are key for customizing and deploying real-world AI solutions effectively.
Key Takeaway
You don’t need to start from scratch!
Dave Voutila showed how Azure AI Foundry makes it easy to fine-tune existing models and use distillation techniques without needing deep ML expertise. These tools let you iterate faster, test ideas, and deploy solutions at scale—all with efficiency in mind.
How Can I Get Started?
Here are a few practical links:
What’s New in Azure AI Foundry?
Here are some of the latest updates:
- Streamlined fine-tuning workflows: Making it easier for developers to adapt models without complex setup
- Improved distillation pipelines: To help create compact, high-performing versions of larger models
- More robust documentation and examples: Great for newcomers exploring use cases
- Optimized deployment options: Especially useful for edge and resource-constrained environments
My A-Ha Moment
Before this episode, the terms “fine-tuning” and “distillation” sounded intimidating. But Dave explained them in such a clear, practical way that I realized—it’s all about enhancing what already exists.
I learned that I don’t have to build AI from scratch. Using Azure AI Foundry, I can tune a model to my own needs and even shrink it for performance. That gave me the confidence to try building on top of existing models without fear. My a-ha moment? Realizing that responsible innovation is totally doable—even for students like me!
Coming Up Next Week
Next episode, we go deeper into research & innovation with SeokJin Han and Saumil Shrivastava. They'll talk about the MCP Server and the Magentic-UI project, which is shaping the future of human-in-the-loop AI.
Don’t miss it!
Join the Community
You’re not alone on this journey. Connect with other developers and learn together:
About Me
I'm Sharda Kaur, a Gold Microsoft Learn Student Ambassador passionate about AI and cloud. I enjoy sharing what I learn to help others grow.
Thanks for reading! I’ll be back next week with another episode recap from Model Mondays!
