Microsoft Azure provides a range of cloud offerings to support the deployment and management of Large Language Models (LLMs). Here are some key services and features:
• Azure AI: This platform allows users to deploy pre-trained models like Meta’s Llama 2, enabling the evaluation, customization, and deployment of LLMs for commercial applications. Azure AI is designed to help developers create generative AI applications responsibly, with built-in tools and technologies for safety and trustworthiness https://techcommunity.microsoft.com/t5/ai-machine-learning-blog/deploy-large-language-models-responsibly-with-azure-ai/ba-p/3876792.
• Azure Databricks: It offers an environment for developing generative AI and LLMs, supporting the entire AI lifecycle from data collection to model serving. Azure Databricks features Unity Catalog for governance, MLflow for model tracking, and Databricks Model Serving for deploying LLMs https://learn.microsoft.com/en-us/azure/databricks/generative-ai/generative-ai.
• Azure AI Infrastructure: Microsoft provides access to high-end GPU virtual machine clusters, which is beneficial for startups and high-growth companies focusing on training and developing LLMs and deep learning models.
• Azure OpenAI Service: This service empowers various applications like Microsoft Office 365, chatbots, and search engines with generative AI, facilitated by LLMs https://azure.microsoft.com/en-us/blog/azure-sets-a-scale-record-in-large-language-model-training/.
• LLMOps: Azure offers resources for application development, cloud migration, generative AI, and LLMOps, keeping up with the latest innovations in prompt engineering, fine-tuning, and LLM app development https://azure.microsoft.com/en-us/blog/the-new-ai-imperative-unlock-repeatable-value-for-your-organization-with-llmops/.
These offerings are part of Microsoft's commitment to providing scalable, secure, and responsible AI capabilities to its users.