The Future Of AI: Deconstructing Contoso Chat - Learning GenAIOps in practice
How can AI engineers build applied knowledge for GenAIOps practices? By deconstructing working samples! In this multi-part series, we deconstruct Contoso Chat (a RAG-based retail copilot sample) and use it to learn the tools and workflows to streamline out end-to-end developer journey using Azure AI Foundry.The Future of AI: Power Your Agents with Azure Logic Apps
Building intelligent applications no longer requires complex coding. With advancements in technology, you can now create agents using cloud-based tools to automate workflows, connect to various services, and integrate business processes across hybrid environments without writing any code.Azure AI Foundry: Empowering Scientific Discovery with AI
Azure AI Foundry is enabling scientific discovery with the introduction of three groundbreaking models from Microsoft Research: Aurora, MatterSim, and TamGen. These models, available starting January 20, 2025, offer transformative capabilities in weather forecasting, materials simulation, and drug design. By providing access to these advanced tools, Azure AI Foundry is enabling researchers and developers to explore new frontiers and accelerate the pace of innovation.Introducing Phi-4: Microsoft’s Newest Small Language Model Specializing in Complex Reasoning
Today we are introducing Phi-4, our 14B parameter state-of-the-art small language model (SLM) that excels at complex reasoning in areas such as math, in addition to conventional language processing. Phi-4 is the latest member of our Phi family of small language models and demonstrates what’s possible as we continue to probe the boundaries of SLMs. Phi-4 is available on Azure AI Foundry and on Hugging Face. Phi-4 Benchmarks Phi-4 outperforms comparable and larger models on math related reasoning due to advancements throughout the processes, including the use of high-quality synthetic datasets, curation of high-quality organic data, and post-training innovations. Phi-4 continues to push the frontier of size vs quality. Phi-4 is particularly good at math problems, for example here are the benchmarks for Phi-4 on math competition problems: Phi-4 performance on math competition problems To see more benchmarks read the newest technical paper released on arxiv. Enabling AI innovation safely and responsibly Building AI solutions responsibly is at the core of AI development at Microsoft. We have made our robust responsible AI capabilities available to customers building with Phi models, including Phi-3.5-mini optimized for Windows Copilot+ PCs. Azure AI Foundry provides users with a robust set of capabilities to help organizations measure, mitigate, and manage AI risks across the AI development lifecycle for traditional machine learning and generative AI applications. Azure AI evaluations in AI Foundry enable developers to iteratively assess the quality and safety of models and applications using built-in and custom metrics to inform mitigations. Additionally, Phi users can use Azure AI Content Safety features such as prompt shields, protected material detection, and groundedness detection. These capabilities can be leveraged as content filters with any language model included in our model catalog and developers can integrate these capabilities into their application easily through a single API. Once in production, developers can monitor their application for quality and safety, adversarial prompt attacks, and data integrity, making timely interventions with the help of real-time alerts. Phi-4 in action One example of the mathematical reasoning Phi-4 is capable of is demonstrated in this problem. Start Exploring Phi-4 is currently available on Azure AI Foundry and Hugging Face, take a look today.The Future of AI: Horses for Courses - Task-Specific Models and Content Understanding
Task-specific models are designed to excel at specific use cases, offering highly specialized solutions that can be more efficient and cost-effective than general-purpose models. These models are optimized for particular tasks, resulting in faster performance and lower latency, and they often do not require prompt engineering or fine-tuning.New evaluation tools for multimodal apps, benchmarking, CI/CD integration and more
If not designed carefully, GenAI applications can produce outputs that have errors, lack grounding in verifiable data, or are simply irrelevant or incoherent, resulting in poor customer experiences and attrition. Even worse, an application’s outputs could perpetuate bias, promote misinformation, or expose organizations to malicious attacks. By conducting proactive risk evaluations throughout the GenAIOps lifecycle, organizations can better-understand and mitigate risks to achieve more secure, safe, and trustworthy customer experiences. Whether you’re evaluating and comparing models at the start of an AI project or running a final evaluation of your application to demonstrate production-readiness, every evaluation has these key components: the evaluation target, whether a base model or an application in development or in production, it’s the thing you’re trying to assess, the evaluation data, comprised of inputs and generated outputs that form the basis of evaluation, and evaluators, or metrics, that help measure and compare performance in a consistent, interpretable way. Today, we’re excited to announce enhancements across these key components, making evaluations in Azure AI Foundry even more comprehensive and accessible for a broad set of generative AI use cases. Here’s a quick summary before we dive into details: Simplify model selection with enhanced benchmarks and model evaluations We’ve enhanced the model benchmarking experience in Azure AI Foundry, adding new performance metrics (e.g. latency, estimated cost, and throughput) and generation quality metrics. This allows users to compare base models across diverse criteria, to better understand potential trade-offs. Evaluate and compare base models using your own private data. This capability simplifies the model selection process by allowing organizations to compare how different models behave in real-world settings and assess which models align best with their unique requirements. Drive robust, measurable insights with new and advanced evaluators New risk and safety evaluations for image and multimodal content provide an out-of-the-box way to assess the frequency and severity of harmful content in generative AI interactions containing imagery. These evaluations can help inform targeted mitigations and demonstrate production-readiness. Evaluations for quality metrics are now generally available for text-based generative AI models and apps. Using either no-code and/or code-first experiences, users can assess generative AI models and applications for key quality attributes such as groundedness, coherence, recall, and fluency. Operationalize evaluations as part of your GenAIOps A new Python API allows developers to run built-in and custom text-based evaluations remotely in the cloud, streamlining the evaluation process at scale with the convenience of easy CI/CD integration. GitHub Actions for GenAI evaluations enable developers to use GitHub Actions to run automated evaluations of their models and applications, for faster experimentation and iteration within their coding environment. In related news, continuous online evaluations of generated outputs are now available, allowing teams to monitor and improve AI applications in production. Additionally, as applications transition from development to production, developers will soon have the capability to document and share evaluation results along with other key information about their fine-tuned models or applications through AI reports. With these expanded capabilities, cross-functional teams are empowered to iterate, launch, and govern their GenAI applications with greater observability and confidence. New benchmarking experience in Azure AI Foundry Picture this: You’re a developer exploring the Azure AI model catalog, trying to find the right fit for your use case. You use search filters, explore available models, and read the model cards to identify strong contenders, but you’re still not sure which model to choose. Why? Selecting the optimal model for an application isn't just about learning as much as you can about each individual model. Organizations need to understand and compare performance from multiple angles—accuracy, relevance, coherence, cost, and computational efficiency—to understand the trade-offs. Now, an enhanced benchmarking experience enables developers to view comprehensive, detailed performance data for models in the Azure AI model catalog while also allowing for direct comparison across multiple models. This provides developers with a clearer picture of each model’s relative performance across critical performance metrics to identify models that meet business requirements. Azure AI Foundry supports four categories of metrics to facilitate robust comparisons: Quality: Assess the accuracy, groundedness, coherence, and relevance of each model’s output. Cost: Assess estimated costs associated with deploying and running the models. Latency: Assess the response times for each model to understand speed and responsiveness. Throughput: Assess the number of tasks each model can process within a specific time frame, to gauge scalability and efficiency. Learn more in our documentation. Evaluate and compare models using your own data Once you have compared various models using benchmarks on public data, you might still be wondering which model will perform best for your specific use case. At this point, it would be more helpful to compare each model using your own test dataset that reflects the inputs and outputs typical of your intended use case. We’re excited to provide developers with an easier way to do just that. Now, developers can easily evaluate and compare both base models and fine-tuned models from within the Azure AI Foundry portal. This is also helpful when comparing base models to fine-tuned models, to see the impact of your training data. With this update, developers can assess models using their own test data and pre-built quality and safety evaluators, for easier side-by-side model comparisons and data-driven decisions when building GenAI applications. Key components of this update, now available in public preview, include: A new entry point in the Azure AI model catalog to guide users through model evaluation. Expanded support for Azure OpenAI Service and Models as a Service (Maas) models, so developers can evaluate these models and user-defined prompts directly within Azure AI Foundry portal. Simplified evaluation setup wizard, so both experienced GenAI developers and those new to GenAI can navigate and evaluate models with ease. New tool for real-time test data generation, helping developers rapidly create sample data for evaluation purposes. Enhanced evaluation results page to help developers visualize and quickly grasp the tradeoffs between various evaluation metrics. Learn more in our documentation. Evaluate for risk and safety in image and multimodal content Risk and safety evaluations for images and multimodal content is now available in public preview in Azure AI Foundry. These evaluations can help organizations assess the frequency and severity of harmful content in human and AI-generated outputs to prioritize relevant risk mitigations. For example, these evaluations can help assess content risks in cases where 1) text inputs yield image outputs, 2) a combination of image and text inputs produce text outputs, and 3) images containing text (like memes) generate text and/or image outputs. Azure AI Foundry provides AI-assisted evaluators to streamline these evaluations at scale, where each evaluator functions like a grading assistant, using consistent and predefined grading instructions to assess large datasets of inputs and outputs across specific target metrics. Today, organizations can use these evaluations to assess generated outputs for hateful or unfair, violent, sexual, and self-harm-related content, as well as protected materials that may present infringement risks. These evaluators use a large multimodal language model hosted by Microsoft to not only grade the test datasets but also provide explanations for the evaluation results so they are interpretable and actionable. Making evaluations actionable is essential. Evaluation insights can help organizations compare base models and fine-tuned models to see which models are a better fit for their application. Or, they can help inform proactive steps to mitigate risk, such as activating image and multimodal content filters in Azure AI Content Safety to detect and block harmful content in real-time. After making changes, users can re-run an evaluation and compare the new scores to their baseline results side-by-side to understand the impact of their work and demonstrate production readiness for stakeholders. Learn more in our documentation. Evaluate GenAI models and applications for quality We’re excited to announce the general availability of quality evaluators for GenAI in Azure AI Foundry, accessible through the code-first Azure AI Foundry SDK experience and no-code Azure AI Foundry portal. These evaluators provide a scalable way to assess models and applications against key performance and quality metrics. This update also includes improvements to pre-existing AI-assisted metrics as well as explanations for evaluation results to help ensure they are interpretable and actionable. Generally available evaluators include: AI-assisted evaluators (these require an Azure OpenAI deployment to assist the evaluation), which are commonly used for retrieval augmented generation (RAG) and business and creative writing scenarios: • Groundedness • Retrieval • Relevance • Coherence • Fluency • Similarity Natural Language Processing (NLP) evaluators, which support assessments for the accuracy, precision, and recall of generative AI: • F1 score • ROUGE score • BLEU score • GLEU score • METEOR score Learn more in our documentation. Announcing a Python API for remote evaluation Previously, developers could only run local evaluations on their own machines when using the Azure AI Foundry SDK. Now, we're providing developers with a new, simplified Python API to run remote evaluations in the cloud. This API supports both built-in and custom prompt-based evaluators, allowing for scalable evaluation runs, seamless integration into CI/CD pipelines, and a more streamlined evaluation workflow. Plus, remote evaluation means developers don’t need to manage their own infrastructure for orchestrating evaluations. Instead, they can offload the task to Azure. Learn more in our documentation. GitHub Actions for GenAI evaluations are now available Given trade-offs between business impact, risk and cost, you need to be able to continuously evaluate your AI applications and run A/B experiments at scale. We are significantly simplifying this process with GitHub Actions that can be integrated seamlessly into existing CI/CD workflows in GitHub. With these actions, you can now run automated evaluations after each commit, using the Azure AI Foundry SDK to assess your applications for metrics such as groundedness, coherence, and fluency. First announced at GitHub Universe in October, these capabilities are now available in public preview. GitHub Actions for online A/B experimentation are available to try in private preview. These enable developers to seamlessly and automatically run A/B experiments comparing different models, prompts, and/or general UX changes to an AI application after deploying to production as part of a CD workflow. Analysis via out-of-the-box model monitoring metrics and custom metrics is seamless, with results posted back directly to GitHub. To participate in the private preview please sign up here. Build production-ready GenAI apps with Azure AI Foundry Want to learn about more ways to build trustworthy AI applications? Here are other exciting announcements from Microsoft Ignite to support your GenAIOps and governance workflows: Explore tracing and debugging capabilities to drive continuous improvement Monitor and improve GenAI apps in production Document and share evaluation results with business stakeholders Whether you’re joining in person or online, we can’t wait to see you at Microsoft Ignite 2024. We’ll share the latest from Azure AI and go deeper into best practices for evaluations and trustworthy AI in these sessions: Microsoft Ignite Keynote Trustworthy AI: Future trends and best practices Trustworthy AI: Advanced risk evaluation and mitigation Azure AI and the dev toolchain you need to infuse AI in all your apps Simulate, evaluate, and improve GenAI outputs with Azure AI Foundry _________ Please note: This article was edited on Dec 30, 2024 to reflect the availability of risk and safety evaluations for images in public preview in Azure AI Foundry. This feature was previously announced as "coming soon" at Microsoft Ignite.Announcing management center and other tools to secure and govern Azure AI Foundry
We’re pleased to share new security and IT governance capabilities in Azure AI Foundry that can help organizations build and scale GenAI solutions that are secure by default, including a new management center, granular networking controls, and the general availability of data and service connections.
Distillation: Turning Smaller Models into High-Performance, Cost-Effective Solutions
by Vishal Yadav, Nikhil Pandey Introduction Large Language Models (LLMs) have transformed the landscape of natural language processing (NLP) with their ability to understand and generate human-like text. However, their size and complexity often pose challenges in terms of deployment, speed, and cost. Usually for specialized niche tasks, we end up deploying the best available model even though we don’t utilize all its capabilities. This is where distillation comes in, offering a method to create (fine-tune) smaller, customized, more efficient models, while retaining much of the performance of a significantly larger state-of-the-art model. What is distillation? Distillation is a technique designed to transfer knowledge of a large pre-trained model (the "teacher") into a smaller model (the "student"), enabling the student model to achieve comparable performance to the teacher model. This technique allows users to leverage the high quality of larger LLMs, while reducing inference costs in a production environment, thanks to the smaller student model. How distillation works? In distillation, knowledge can be transferred from teacher to student model in several ways. Here, we specifically discuss response-based, offline distillation, where the student model learns to mimic the output (only predictions) of the teacher model, and the teacher model is not trained during distillation. Teacher Model: A large, high-capacity teacher model that is already pre-trained on massive datasets. This model has learnt rich representations and complex patterns from the data which allows it to generalize well even on unseen tasks. Knowledge Extraction: The teacher model generates outputs based on given inputs, which are then used as training data for the student model. This involves not just mimicking outputs but also understanding the underlying reasoning processes. Student Model Training: A smaller student model is trained using the extracted knowledge as a guide. The student model learns to mimic the teacher model's behavior and predictions on specific tasks. Advantages Reduced Size: The resulting student model is significantly smaller, making it easier to deploy in resource-constrained environments. Lower Cost: Running smaller models incurs lower operational costs while maintaining competitive performance levels. Task-Specific Optimization: Distillation can be tailored for specific applications, enhancing efficiency and accuracy. Performance: Smaller models exhibit significantly lower latency compared to larger models, which in turn boosts the throughput of the deployment. Customization: Distillation allows users to select desirable traits from multiple larger models and transfer them to smaller models. Personalization: Personality traits can be incorporated into the model, enabling it to respond with relevant answers when queried about its personality. Synthetic Data Generation: At scale data generation can be done either only for labels or from scratch using just seed/meta data. Generalization: Distillation can help student models generalize better by learning from the teacher model's knowledge and avoiding overfitting. Improved Multilingual Capabilities: The multilingual performance of smaller models can be significantly enhanced with the help of teacher models making them suitable for global applications. Distillation in Azure AI Foundry Distillation as a Service is now supported on Azure allowing a variety of task types and more to be added soon. Following tasks are supported. Summarization: Given a document (article) to summarize, generate an entity-dense summary of the document. Conversational Assistant: Generate AI assistant responses on single-turn and multi-turn conversational datasets. To generate each response, the available chat history and the current user prompt are utilized. Natural Language Understanding (NLU) o MATH: Generate numeric answers to math problems. o Natural Language Inference (NLI): Given premise and hypothesis, determine if premise entails the hypothesis, or contradicts the hypothesis, or is neutral i.e. neither entails not contradicts the hypothesis. o Multiple-Choice Question Answering: Given question and answer choices, determine the correct answer choice. Distillation Process Overview of the two-step distillation process: (1) Generate synthetic data using a task-specific, elaborate prompt (2) Train (and infer from) the student model using a shorter prompt (Figure source: https://arxiv.org/pdf/2410.18588) The distillation process involves two main steps: generate high quality synthetic data (labels) using the teacher model, followed by instruction-based finetuning of the student model. Data Generation High-quality data generation is crucial for the student model's performance. Azure provides a proprietary library of advanced prompts, to generate high-quality synthetic data for all supported tasks, utilizing techniques such as Chain of Thought (CoT) or Chain of Density (CoD), and other best practices. This option can be enabled by passing the `enable_chain_of_thought` parameter while invoking the distillation pipeline, ensuring reasoning-based answers and consequently high-quality data for distillation. Instruction Fine-Tuning The next step is to fine-tune the smaller model using the task-specific generated data. This involves using a concise, task-specific prompt and training with the input and generated output (excluding reasoning steps). These innovations ensure significant performance gains for a given task while minimizing the cost (number of tokens) for the user. When using user-provided prompts, the same prompt is applied in both data generation and fine-tuning. Distillation Code Snippet Distillation is supported by the Azure SDK and CLI. Support for this was added in version 1.22.0 of azure-ai-ml. Ensure that the azure-ai-ml package is >= 1.22.0 before using the code snippet below. Model Offerings Teacher Models Currently Meta Llama 3.1 405B Instruct is supported as the teacher model for distillation. Student Models Currently Meta Llama 3.1 8B Instruct is supported as the student model for distillation. Soon all Microsoft’s Phi 3 and 3.5 Instruct series models will also be available for distillation. The following table demonstrates our current and upcoming student model offerings. Student Model Region Availability Meta Llama 3.1 8B Instruct West US 3 Available Phi 3/3.5 Instruct East US 2 Coming Soon At the time of this writing, fine-tuning of Meta Llama 3.1 Instruct series of models, and deployment of such fine-tuned models, is only available in West US 3 region. Whereas fine-tuning of Microsoft’s Phi 3 Instruct series of models, and deployment of such fine-tuned models, is only available in East US 2 region. Ensure your AI Foundry project is setup in the appropriate region for your selected student model. Notebooks Distilling Large Language Models for NLI Tasks: A Practical Guide Notebook - Distillation with Large Language Models This notebook provides a comprehensive guide on how to distil a large teacher model into a smaller student model, specifically for Natural Language Inference (NLI) tasks. It uses the Meta Llama 3.1 405B Instruct as the teacher and the Meta Llama 3.1 8B Instruct as the student model. Key Highlights Teacher and Student Models: The process uses Meta Llama 3.1 405B Instruct as the teacher model and Meta Llama 3.1 8B Instruct as the student model. Prerequisites: Ensure you have subscribed to the required models and set up an AI Foundry project in the West US 3 region for distillation of a Meta Llama 3.1 8B Instruct student model. SDK Installation: Install necessary SDKs such as azure-ai-ml, azure-identity, and mlflow. Dataset Preparation: Use the ConjNLI dataset from Hugging Face for training and validation. Distillation Job: Configure and run the distillation job to transfer knowledge from the teacher to the student model. Deployment: Optionally, deploy the distilled model to a serverless endpoint and perform sample inferences. This notebook simplifies the complex task of model distillation, making it accessible even to those new to NLP and model training. Results Using the ConjNLI dataset and Chain-Of-Thought (CoT) distillation, we obtain the following accuracy (%) metrics. Dataset Student Model Teacher (405B) with CoT Prompting Student with CoT Prompting Student Distilled on CoT-prompted Teacher Output ConjNLI (dev) Meta Llama 3.1 8B Instruct 69.98 52.81 63.88 ConjNLI (dev) Phi 3 Mini 128k Instruct 69.98 43.98 57.78 Distillation with the Meta Llama 3.1 8B Instruct and Phi 3 Mini 128k Instruct student models provides approximately 21% and 31% improvement respectively over directly prompting the student model using CoT prompt. For detailed results on other datasets and tasks, we refer the user to check the published results in our knowledge distillation paper. Conclusion Distillation represents a significant step forward in development and deployment of LLM/SLM at scale. By transferring the knowledge from a large pre-trained model (teacher) to a smaller, more efficient model (student), distillation offers a practical solution to the challenges of deploying large models, such as high costs and complexity. This technique not only reduces model size and operational costs but also enhances the performance of student models for specific tasks. The support for distillation on Azure AI Foundry further simplifies the process, making it accessible for various applications, such as summarization, conversational assistance, and natural language understanding tasks. Furthermore, the detailed, hands-on example notebooks provided in Azure Github can help facilitate easier adoption. In summary, distillation not only bridges the gap between generalist understanding and specialized application but also makes the way for a more sustainable and practical approach to leveraging LLMs in real-world scenarios.Announcing Model Fine-Tuning Collaborations: Weights & Biases, Scale AI, Gretel and Statsig
As AI continues to transform industries, the ability to fine-tune models and customize them for specific use cases has become more critical than ever. Fine-tuning can enable companies to align models with their unique business goals, ensuring that AI solutions deliver results with greater precision However, organizations face several hurdles in their model customization journey: Lack of end-to-end tooling: Organizations struggle with fine-tuning foundation models due to complex processes, and the absence of tracking and evaluation tools for modifications. Data scarcity and quality: Limited access to large, high-quality datasets, along with privacy issues and high costs, complicate model training and fine-tuning. Shortage of fine-tuning expertise and pre-trained models: Many companies lack specialized knowledge and access to refined models for fine-tuning. Insufficient experimentation tools: A lack of tools for ongoing experimentation in production limits optimization of key variables like model diversity and operational efficiency. To address these challenges, Azure AI Foundry is pleased to announce new collaborations with Weights & Biases, Scale AI, Gretel and Statsig to streamline the process of model fine-tuning and experimentation through advanced tools, synthetic data and specialized expertise. Weights & Biases integration with Azure OpenAI Service: Making end-to-end fine-tuning accessible with tooling The integration of Weights & Biases with Azure OpenAI Service offers a comprehensive end-to-end solution for enterprises aiming to fine-tune foundation models such as GPT-4, GPT-4o, and GPT-4o mini. This collaboration provides a seamless connection between Azure OpenAI Service and Weights and Biases Models which offers powerful capabilities for experiment tracking, visualization, model management, and collaboration. With the integration, users can also utilize Weights and Biases Weave to evaluate, monitor, and iterate on the performance of their fine-tuned models powered AI applications in real-time. Azure's scalable infrastructure allows organizations to handle the computational demands of fine-tuning, while Weights and Biases offers robust capabilities for fine-tuning experimentation and evaluation of LLM-powered applications. Whether optimizing GPT-4o for complex reasoning tasks or using the lightweight GPT-4o mini for real-time applications, the integration simplifies the customization of models to meet enterprise-specific needs. This collaboration addresses the growing demand for tailored AI models in industries such as retail and finance, where fine-tuning can significantly improve customer service chatbots or complex financial analysis. Azure Open AI Service and Weights & Biases integration is now available in public preview. For further details on Azure OpenAI Service and Weights & Biases integration including real-world use-cases and a demo, refer to the blog here. Scale AI and Azure Collaboration: Confidently Implement Agentic GenAI Solutions in Production Scale AI collaborates with Azure AI Foundry to offer advanced fine-tuning and model customization for enterprise use cases. It enhances the performance of Azure AI Foundry models by providing high-quality data transformation, fine-tuning and customization services, end-to-end solution development and specialized Generative AI expertise. This collaboration helps improve the performance of AI-driven applications and Azure AI services such as Azure AI Agent in Azure AI Foundry, while reducing production time and driving business impact. "Scale is excited to partner with Azure to help our customers transform their proprietary data into real business value with end-to-end GenAI Solutions, including model fine-tuning and customization in Azure." Vijay Karunamurthy, Field CTO, Scale AI Checkout a demo in BRK116 session showcasing how Scale AI’s fine-tuned models can improve agents in Azure AI Foundry and Copilot Studio. In the coming months, Scale AI will offer fine-tuning services for Azure AI Agents in Azure AI Foundry. For more details, please refer to this blog and start transforming your AI initiatives by exploring Scale AI on the Azure Marketplace. Gretel and Azure OpenAI Service Collaboration: Revolutionizing data pipeline for custom AI models Azure AI Foundry is collaborating with Gretel, a pioneer in synthetic data and privacy technology, to remove data bottlenecks and bring advanced AI development capabilities to our customers. Gretel's platform enables Azure users to generate high-quality datasets for ML and AI through multiple approaches - from prompts and seed examples to differential privacy-preserved synthetic data. This technology helps organizations overcome key challenges in AI development including data availability, privacy requirements, and high development costs with support for structured, unstructured, and hybrid text data formats. Through this collaboration, customers can seamlessly generate datasets tailored to their specific use cases and industry needs using Gretel, then use them directly in Azure OpenAI Service for fine-tuning. This integration greatly reduces both costs and time compared to traditional data labeling methods, while maintaining strong privacy and compliance standards. The collaboration enables new use cases for Azure AI Foundry customers who can now easily use synthetic data generated by Gretel for training and fine-tuning models. Some of the new use cases include cost-effective improvements for Small Language Models (SLMs), improved reasoning abilities of Large Language Models (LLMs), and scalable data generation from limited real-world examples. This value is already being realized by leading enterprises. “EY is leveraging the privacy-protected synthetic data to fine-tune Azure OpenAI Service models in the financial domain," said John Thompson, Global Client Technology AI Lead at EY. "Using this technology with differential privacy guarantees, we generate highly accurate synthetic datasets—within 1% of real data accuracy—that safeguard sensitive financial information and prevent PII exposure. This approach ensures model safety through privacy attack simulations and robust data quality reporting. With this integration, we can safely fine-tune models for our specific financial use cases while upholding the highest compliance and regulatory standards.” The Gretel integration with Azure OpenAI Service is available now through Gretel SDK. Explore this blog describing a finance industry case study and checkout details in technical documentation for fine-tuning Azure OpenAI Service models with synthetic data from Gretel. Visit this page to learn more Statsig and Azure Collaboration: Enabling Experimentation in AI Applications Statsig is a platform for feature management and experimentation that helps teams manage releases, run powerful experiments, and measure the performance of their products. Statsig and Azure AI Foundry are collaborating to enable customers to easily configure and run experiments (A/B tests) in Azure AI-powered applications, using Statsig SDKs in Python, NodeJS and .NET. With these Statsig SDKs, customers can manage the configuration of their AI applications, manage the release of new configurations, run A/B tests to optimize model and application performance, and automatically collect metrics at the model and application level. Please check out this page to learn more about the collaboration and get detailed documentation here. Conclusion The new collaborations between Azure and Weights & Biases, Scale AI, Gretel and Statsig represent a significant step forward in simplifying the process of AI model customization. These collaborations aim to address the common pain points associated with fine-tuning models, including lack of end-to-end tooling, data scarcity and privacy concerns, lack of expertise and experimentation tooling. Through these collaborations, Azure AI Foundry will empower organizations to fine-tune and customize models more efficiently, ultimately enabling faster, more accurate AI deployments. Whether it’s through better model tracking, access to synthetic data, or scalable data preparation services, these collaborations will help businesses unlock the full potential of AI.Ignite 2024: Streamlining AI Development with an Enhanced User Interface, Accessibility, and Learning Experiences in Azure AI Foundry portal
Announcing Azure AI Foundry, a unified platform that simplifies AI development and management. The platform portal (formerly Azure AI Studio) features a revamped user interface, enhanced model catalog, new management center, improved accessibility and learning, making it easier than ever for Developers and IT Admins to design, customize, and manage AI apps and agents efficiently.
