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TLDR:
- Azure is introducing updates to the Phi model family, including a new MoE model and 20+ languages.
- A streamlined RAG pipeline with integrated data prep and embedding is being introduced.
Boost your AI with Azure’s new Phi model, streamlined RAG, and custom generative AI models
As developers continue to develop and deploy AI applications at scale across organizations, Azure is committed to delivering unprecedented choice in models as well as a flexible and comprehensive toolchain to handle the unique, complex and diverse needs of modern enterprises. Several key updates have been announced to help developers create AI solutions with greater choice and flexibility using the Azure AI toolchain.
Phi Model Updates
Azure is introducing a new model to the Phi family, Phi-3.5-MoE, a Mixture of Experts (MoE) model. This new model combines 16 smaller experts into one, delivering improvements in model quality and lower latency. The Phi vision model now supports multi-frame support, allowing reasoning over multiple input images, unlocking new scenarios like identifying differences between images.
AI21 Jamba Model as a Service
Two new open models, Jamba 1.5 Large and Jamba 1.5, are available in the Azure AI model catalog. These models utilize the Hybrid Mamba-Transformer architecture and excel in managing extended contexts ideal for various industries.
Streamlined RAG Pipeline
Integrated vectorization in Azure AI Search automates and streamlines data preparation and embedding processes, enabling organizations to offer turnkey RAG systems as solutions for new projects.
Custom Generative Models
Azure AI Document Intelligence now allows the extraction of custom fields for unstructured documents with high accuracy by building and training a custom generative model. This new ability uses generative AI to extract user-specified fields from documents.
Text to Speech (TTS) Avatar
Text to Speech (TTS) Avatar, a capability of Azure AI Speech service, is now generally available and brings natural-sounding voices and photorealistic avatars to life, enhancing customer engagement and overall experience.
Azure Machine Learning Resources in VS Code
The general availability of the VS Code extension for Azure Machine Learning allows developers to build, train, deploy, and manage machine learning models with Azure Machine Learning directly from their favorite VS Code setup.
Conversational PII Detection Service
The general availability of Conversational PII Detection Service in Azure AI Language enhances Azure AI’s ability to identify and redact sensitive information in conversations, starting with English language.
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