Weights & Biases on Microsoft Azure

Weights & Biases on Azure empowers teams to streamline AI development. You can train, fine-tune, and manage models from experimentation to deployment, and also evaluate, iterate and monitor LLM-powered applications. Leverage Azure’s powerful infrastructure and the best-in-class AI developer platform from Weights & Biases to drive innovation.

Our partnership with Weights & Biases enhances Azure AI’s capabilities by streamlining model development, fine-tuning, and monitoring through seamless integration. By leveraging powerful GenAIOps tools from Weights & Biases, developers can easily track, version, and scale generative AI models from experimentation to production. This collaboration enables teams to rapidly iterate and optimize their workflows while managing models directly within Azure AI, including Azure OpenAI Service, Phi-3, and the top open-source models, making it easier to deliver high-impact AI applications for enterprise-scale innovation.

Asha Sharma

CVP AI Platform, Microsoft

Fine-tune LLMs with Azure OpenAI Service and W&B Models

W&B Models now integrates with Azure OpenAI Service, enabling seamless fine-tuning of advanced models like GPT-4 and GPT-4o for tailored organizational needs with powerful experiment tracking and collaboration features. And now with the option to use W&B Models on a dedicated Azure tenant, you can ensure that all your data is securely managed within the Azure ecosystem.

Why Weights & Biases on Microsoft Azure

Accelerate GenAI innovation

Leverage W&B Models with Azure OpenAI Service to fine-tune cutting-edge LLMs then evaluate, iterate, and monitor them with W&B Weave so you can be more confident delivering LLM-powered apps to production.

Create an AI system of record

Seamlessly integrate W&B Models with Azure’s robust infrastructure to create a centralized registry that stores and provides versioning, aliases, lineage tracking, and governance of models and datasets.

Foster knowledge sharing

Foster a culture of innovation with Weights & Biases shared workspaces and Microsoft Teams. Enable seamless knowledge sharing and real-time communication across AI teams, accelerating breakthroughs.

Safeguard AI development

Secure sensitive data with Microsoft Entra ID integrated access controls and a dedicated tenant on Azure. Ensure compliance and responsible AI governance through comprehensive audit trails.

How it works

Features

Seamless integration

Deploy W&B Models on a dedicated Azure tenant from the Azure Portal and benefit from data isolation, private connectivity, and built-in integrations with Azure AI Studio, Azure ML, Azure OpenAI Service, Azure Kubernetes Service, and other Azure AI services.

Enhanced performance

Leverage Azure’s purpose-built infrastructure to train with W&B Models and deploy models up to 50x faster. Scale your experiments effortlessly with auto-scaling clusters and optimized resource allocation.

Scalable experiment tracking

Automatically log hyperparameters, metrics, and artifacts for all your Azure AI Studio and Azure ML runs in W&B Models. Visualize and compare experiments to make data-driven decisions.

LLM fine-tuning

Fine-tune LLMs using W&B Models features, including logging, hyperparameter tuning, and visualization tools integrated with Azure OpenAI Service.

Central repository for models and datasets

Version and manage your models and datasets seamlessly with W&B Registry and Azure AI Studio. Ensure reproducibility and traceability throughout the model lifecycle.

Collaborative workspaces

Foster teamwork with shared workspaces, experiment commenting, reports, and integration between W&B Models and Microsoft Teams for real-time notifications.

LLM evaluations

Evaluate, iterate, and monitor LLM-powered applications with W&B Weave, optimized for Azure AI infrastructure. Gain deep insights into model performance and behavior.

Governance Framework

Implement robust governance with fine-grained access controls, audit trails, and integration with Weights & Biases and Microsoft Entra ID.

Accelerating AI Innovation in Autonomous Vehicles

Wayve leveraged Weights & Biases and Azure to train autonomous vehicle AI models 50 times faster, significantly accelerating their time-to-market and competitive advantage. Read the full Wayve case study to learn how they implemented end-to-end MLOps with W&B Models and Azure and watch Wayve’s talk at Fully Connected 2024 to learn more.

We actively use W&B Models to track all our experiments, and it’s allowed us to dig deep into comparisons and monitor in real-time exactly what’s happening in training.