Optimize and fine-tune AI agents for production
Intermediate
Data Scientist
AI Engineer
Microsoft Foundry
Learn to evaluate and select fine-tuning methods, recognize agent quality problems, prepare training data, and design optimization strategies using hyperparameter configuration and iterative evaluation.
Learning objectives
By the end of this module, you'll be able to:
- Evaluate and select fine-tuning methods including supervised fine-tuning (SFT), reinforcement fine-tuning (RFT), and direct preference optimization (DPO) based on quality requirements, data availability, and cost constraints.
- Recognize agent quality problems and determine which fine-tuning approach addresses the root cause for format inconsistency, tone misalignment, or reasoning failures.
- Prepare training data by validating format requirements for each fine-tuning method and applying quality principles to create effective datasets.
- Design an optimization strategy by evaluating baseline performance, setting measurable targets, splitting your dataset, and configuring hyperparameters for your chosen method.
Prerequisites
Before starting this module, you should be familiar with fundamental AI concepts and services in Azure.