Microsoft Azure
Weights & Biases integrates with Microsoft Azure OpenAI services, helping teams to manage, debug, and optimize their Azure AI workflows at scale. This guide introduces the W&B integration, what it means for Weave users, its key features, and how to get started.
Key features
- LLM evaluations: Evaluate and monitor LLM-powered applications using Weave, optimized for Azure infrastructure.
- Seamless integration: Deploy W&B Models on a dedicated Azure tenant with built-in integrations for Azure AI Studio, Azure ML, Azure OpenAI Service, and other Azure AI services.
- Enhanced performance: Use Azure’s infrastructure to train and deploy models faster, with auto-scaling clusters and optimized resources.
- Scalable experiment tracking: Automatically log hyperparameters, metrics, and artifacts for Azure AI Studio and Azure ML runs.
- LLM fine-tuning: Fine-tune models with W&B Models.
- Central repository for models and datasets: Manage and version models and datasets with W&B Registry and Azure AI Studio.
- Collaborative workspaces: Support teamwork with shared workspaces, experiment commenting, and Microsoft Teams integration.
- Governance framework: Ensure security with fine-grained access controls, audit trails, and Microsoft Entra ID integration.
Getting started
To use W&B with Azure, add the W&B integration via the Azure Marketplace.
For a detailed guide describing how to integrate Azure OpenAI fine-tuning with W&B, see Integrating Weights & Biases with Azure AI Services.