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This guide shows you how to use W&B with Azure OpenAI to track and evaluate fine-tuning jobs for GPT-3.5 or GPT-4 models. When you integrate W&B, experiment tracking captures metrics, hyperparameters, and training artifacts so you can analyze and improve model performance. You can also use W&B’s evaluation tools to make data-driven decisions about model selection. This guide is for machine learning practitioners who fine-tune Azure OpenAI models and want a systematic way to track and compare runs.
Azure OpenAI fine-tuning metrics

Prerequisites

Before you begin, complete the following:

Workflow overview

The following stages summarize how a typical Azure OpenAI fine-tuning job flows through W&B, from preparing the job through evaluating the resulting model.

Fine-tuning setup

Fine-tuning setup involves the following steps:
  • Prepare training data according to Azure OpenAI requirements.
  • Configure the fine-tuning job in Azure OpenAI.
  • W&B automatically tracks the fine-tuning process and logs metrics and hyperparameters.

Experiment tracking

During fine-tuning, W&B captures:
  • Training and validation metrics.
  • Model hyperparameters.
  • Resource usage.
  • Training artifacts.

Model evaluation

After fine-tuning, use W&B Weave to:
  • Evaluate model outputs against reference datasets.
  • Compare performance across different fine-tuning runs.
  • Analyze model behavior on specific test cases.
  • Make data-driven decisions for model selection.

Real-world example

To see the integration applied end-to-end, explore the following resources:

Additional resources