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MicrosoftIn case you missed it, recent Azure AI innovation announcements
In case you missed it, learn about recent AI announcements Microsoft made at Ignite 2024 including what’s new in Azure AI Governance, Azure AI Model Catalog, Azure AI Search, Fin-Tuning in Azure OpenAI Service, new partner models and more.
Azure AI Governance, Risk, & Compliance
- AI reports will help organizations improve cross-functional observability, collaboration and governance when developing and deploying generative AI (GenAI) apps and fine-tuned models. The Azure AI Foundry SDK and Azure AI Foundry portal will make it easier for organizations to create impact assessments for their AI apps by helping developers assemble key project details, such as model cards, model versions, content safety filter configurations and evaluation metrics, into a unified AI report. These reports can be exported to PDF or SPDX formats, helping development teams demonstrate production readiness within governance, risk and compliance (GRC) workflows and facilitate easier, ongoing audits of apps in production. This update will be in private preview next month.
- Risk and safety evaluations for image content will help users assess the frequency and severity of harmful content in their app’s AI-generated outputs. Specifically, these evaluations will expand existing text-based evaluation capabilities in Azure AI to assess a broader set of interactions with GenAI, such as text inputs that yield image outputs, image and text inputs that yield text outputs, and images that contain text (i.e., memes) as inputs that yield text and/or image outputs. These evaluations will help organizations better understand potential risks and apply targeted mitigations, such as modifying multimodal content filters with Azure AI Content Safety, adjusting grounding data sources or updating their system message before deploying an app to production.
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Azure AI Model Catalog
The Azure AI model catalog is adding the latest AI models from leading innovators, enabling organizations to choose the right model for the right use case. Models from NTT DATA, (generally available) and Bria AI (in preview), help organizations bring generative AI capabilities to their apps, while industry-specific models will empower developers to pursue solutions specific to healthcare, agriculture, manufacturing and finance.
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Azure Essentials
Microsoft launched Azure Essentials to help customers improve the reliability, security and ongoing performance of their cloud and AI investments by providing a single place to access a comprehensive set of resources including tooling, skilling, guidance, reference architectures and best practices. Azure Essentials makes it possible to adopt AI at scale while aligning to Trustworthy AI principles and provides organizations with a clear path to maximize the value of their AI investment.
- AI scenario within the Cloud Adoption Framework equips technical decision-makers with prescriptive guidance to help prepare organizations to deploy AI workloads in production. The Cloud Adoption Framework methodologies have been adapted to Responsible AI principles so customers can build an AI foundation that supports the design, governance and ongoing management of AI workloads. It helps users with everything from developing an adoption strategy to managing AI workloads in production.
- AI workload within the Azure Well-Architected Framework supports architects in decision-making when designing their AI workloads. This new guidance allows AI architects to meet the functional and non-functional requirements for reliability, security, performance efficiency, operational excellence and cost optimization.
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Azure AI Search
Updates to Azure AI Search, in preview, will help developers deliver better AI apps with improved retrieval augmented generation (RAG) performance. Query rewriting, available in preview, and semantic ranker are now powered by new, upgraded language models that deliver better responses and improved app experiences. In addition, Azure AI Search will soon be integrated with GitHub Models, enabling developers to explore and build a RAG application using a free AI Search index, directly from GitHub marketplace.
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Fine-Tuning in AOAI Service
New fine-tuning options in Azure OpenAI Service will enable developers and data scientists to customize models for their business needs. This will include support for fine-tuning GPT-4o and GPT-4o mini on Provisioned and Global Standard deployments, in preview next month. Additionally, developers will be able to leverage an end-to-end distillation workflow using Evaluation, in preview, and Stored Completions, in preview next month, to fine-tune cost-effective models, like GPT-4o mini with outputs from advanced models. Multimodal fine-tuning for GPT-4o with vision is now generally available.
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Partner Models
New partner-enabled, adapted AI models address industry-specific use cases to help organizations across industries transform and accelerate business outcomes. Through the Microsoft Cloud, Microsoft’s industry-specific AI capabilities and a trusted ecosystem of experienced partners, these new adapted AI models will empower customers to use AI technology to address their most pressing needs. Partners leveraging the power of Microsoft’s Phi family of small language models include:
- Bayer, a global enterprise with core competencies in the life science fields of healthcare and agriculture, makes L.Y. Crop Protection available in the Azure AI model catalog, for use by agronomic entities and their partners to advance agronomic knowledge and crop protection label compliance. Agronomists can use the model to enhance farmers’ decision-making processes, helping to drive more sustainable outcomes.
- Cerence, a global industry leader in creating unique, moving experiences for the mobility world, is enhancing its in-vehicle digital assistant technology with fine-tuned small language models (SLMs) within the vehicle’s hardware. The Cerence CaLLM Edge model, available in the Azure AI model catalog, can be used for in-car controls, such as adjusting air conditioning systems, and scenarios that involve limited or no cloud connectivity.
- Rockwell Automation, a global leader in industrial automation and digital transformation, will provide industrial AI expertise via the Azure AI model catalog. The FT Optix Food & Beverage model brings the benefits of industry-specific capabilities to frontline workers in manufacturing, supporting asset troubleshooting in the food and beverage domain.
- Saifr, a RegTech within Fidelity Investment’s innovation incubator, Fidelity Labs, introduces four new models in the Azure AI model catalog, empowering financial institutions to better manage regulatory compliance of broker-dealer communications and investment adviser advertising. The Retail Marketing Compliance model can help ensure marketing materials adhere to industry regulations and standards, while the Risk Interpretation model identifies and helps users understand potential risks in marketing content. The Language Suggestion model provides language suggestions to enhance the compliance of marketing messages, and the Image Detection model assists users with analyzing and verifying the appropriateness of images used in marketing campaigns.
- Siemens Digital Industries Software, which helps organizations of all sizes digitally transform using software, hardware and services from the Siemens Xcelerator business platform, is introducing a new copilot for NX X software. It leverages an adapted AI model that enables users to ask natural language questions, access detailed technical insights and streamline complex design tasks for faster and smarter product development.
- Sight Machine, a leader in data-driven manufacturing and industrial AI, will release the Factory Namespace Manager to the Azure AI model catalog. The model helps manufacturers rename and integrate factory data with their corporate data systems, enabling them to analyze and optimize production alongside supply chain, sales, finance and other corporate functions.
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