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Machine Learning

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AI Agents: Metacognition for Self-Aware Intelligence - Part 9
This blog post, Part 9 in a series on AI agents, introduces the concept of metacognition, or "thinking about thinking," and its application to AI agents. It explains how metacognition enables agents to self-evaluate, adapt, and improve their performance. The post outlines the key components of an AI agent and illustrates metacognition with a travel agent example, demonstrating how it can enhance planning, error correction, and personalization. The post also discusses the Corrective RAG approach and demonstrates code snippets.
ShivamGoyal03
Apr 28, 2025 Place Educator Developer Blog
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Using Neural Network to Learn Profitable Trading in the FOREX Markets
I am using Neural Networks (NN) to teach them how to recognize profitable trading opportunities in the Foreign Exchange (FOREX) markets, using 10 currencies simultaneously. I am using 3rd-order Cubic Splines as input to give the NNs a sense of how the critical variables change over time. I am using free FOREX historical trading data to train the NNs how to trade profitably in the future. I don't just feed the trading levels of the FOREX currency pairs as input to the NNs. Instead, I use a variation of the computed DXY Index for all 10 currencies in order to isolate the value change of each of the individual currencies, using Cubic Splines to detail how those values change over various time periods. The end result is Neural Networks that recognize which currencies to Buy and which ones to Sell at the most profitable times. If anyone is interested in the details, please reach out and I will provide more details.
dbaechtel
Apr 21, 2025 Place AI - Machine Learning
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3Comments
Expanding the Llama 4 Herd: New Models Now Available on Azure AI Foundry
Last week, we kicked off the arrival of Meta’s powerful new Llama 4 models in Azure with the launch of three models across Azure AI Foundry and Azure Databricks. Today, we’re expanding the herd with the addition of two new 17B-parameter instruction-tuned models — now available in the Azure AI Foundry model catalog as Models as a Service (MaaS) endpoints New Models Llama-4-Scout-17B-16E-Instruct A fast, low-latency 17B model with 16 experts — optimized for general-purpose tasks with strong instruction following. Llama-4-Maverick-17B-128E-Instruct-FP8 A larger, more expressive variant with 128 experts and FP8 precision — built for heavier, higher-quality reasoning under constrained compute. Both models are: Hosted as serverless MaaS endpoints — no infrastructure setup required Available on GitHub Models and playground What Makes These Llama 4 Models Special? These models are part of Meta’s mixture-of-experts (MoE) family of Llama 4 variants. Unlike dense models, these MoE architectures selectively activate a subset of model parameters (experts) per token, yielding improved efficiency without sacrificing output quality. Scout-17B-16E offers fast inference for common enterprise workloads like summarization, Q&A, and structured output tasks. Maverick-17B-128E-FP8 introduces aggressive expert scaling and FP8 precision, enabling high-throughput inference with improved energy efficiency. How to Get Started You can find these models in the Azure AI Foundry model catalog — just search for "Llama-4" or navigate to the Meta model family. With a few clicks, you can: Deploy the model as a serverless endpoint Invoke it via the Azure AI Foundry playground Integrate using the Azure OpenAI-compatible REST API or Python SDK Use Cases These 17B models are a great fit for: Knowledge assistant copilots Long-form summarization Table-to-text transformation Conversational agents Internal developer tools Explore More To learn more about last week’s launch of Llama 4 models, including 8B and 70B variants, check out the official Azure blog: Introducing the Llama 4 Herd in Azure AI Foundry and Azure Databricks Try these models today in Azure AI Foundry and let us know what you build!
Naomi Moneypenny
Apr 14, 2025 Place AI - AI Platform Blog
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Scalable and Efficient Fine-Tuning of LLM on Azure ML
https://github.com/james-tn/llm-fine-tuning/tree/main/opensource_llm/single_step Co-Author: Mohamad AL jazaery Why Scalable and Efficient Fine-Tuning Matters Faster Iterations, Shorter Time-to-Value: In today’s competitive AI landscape, time is of the essence. The faster you can fine-tune a model, the quicker you can validate ideas, test hypotheses, and bring solutions to market. High-profile GPU machines are costly: High-performance GPUs and compute clusters don’t come cheap, and their availability is often limited. Efficient fine-tuning techniques, such as model sharding and distributed training, maximize the utilization of these precious resources—ensuring that you get the most out of your infrastructure investment. Choosing the Right Azure ML GPU Compute for the Job: NC or ND? Not all GPU computes are created equal, and choosing the right sku can make or break your training efficiency. ND Series: Ideal for distributed training across multiple nodes, thanks to its Infiniband (IB) connectivity that ensures high-speed communication between nodes like pretraining LLM or finetuning very large model ~70B params. NC Series: Small and medium workload where no heavy interaction between nodes needed like LLM inferencing or mid-size LLM finetuning. Azure GPU Machine Options by Scenario: Scenario Common model size Training Approach Recommended Azure Compute Small-scale fine-tuning < 3B parameters Parameter-efficient tuning NCas_T4_v3 (Tesla T4, 16 GB) Medium-scale fine-tuning 1–5B parameters Full or parameter-efficient NCs_v3 (Tesla V100, 16 GB) Distributed training for medium models 5–10B parameters Full fine-tuning ND_v2 (Tesla V100 NVLINK, 32 GB, InfiniBand) Large-scale fine-tuning (single machine) 10–30B parameters Full or parameter-efficient NC_A100_v4 (A100, 40 GB) Distributed training for very large models 20–70B parameters Full fine-tuning NDasrA100_v4 (A100, 80 GB, HDR InfiniBand) Very large models training (single machine) up to 70B parameters Full or parameter-efficient NCads_H100_v5 (H100 NVL, 94 GB) Massive-scale distributed training > 70B parameters Full fine-tuning ND-H100-v5 (H100, 80 GB, scale-out InfiniBand) Distributed Efficient Training: A Quick Guide When scaling fine-tuning tasks, choosing the right distributed training method is key: DDP (Data Parallelism): Works well when the entire model fits on a single GPU. It replicates the model across multiple GPUs and splits the data for parallel processing. Check experiment 1 in the following section. Model Parallelism: A game-changer for massive models that don’t fit on a single GPU. It shards not only the data but also the model parameters and optimizer states across multiple GPUs, enabling efficient training of models like LLaMA-70B on GPUs with low memory GPUs. Both FSDP and DeepSpeed as libraries excel at implementing advanced forms of model parallelism and memory optimization. Memory Optimization Techniques Gradient Checkpointing: Reduces memory by recomputing activations during the backward pass, trading memory for additional computation. Mixed Precision Training: Reduces memory usage by using FP16 or BF16 instead of FP32, accelerating training while maintaining numerical stability. Supported by both frameworks. Quantization (DeepSpeed Exclusive): Uses INT8 precision for weights and activations, dramatically reducing memory and compute requirements. Offloading (DeepSpeed Exclusive): Offloads optimizer states and model parameters to CPU or NVMe, freeing up GPU memory for computation. Our Experiments: Pushing the Limits of Scalability Experiment 1: Distributed Training on Multiple Nodes using DDP We conducted an experiment to fine-tune the Llama-3.1-8B model using LoRA (Low-Rank Adaptation) on Azure ML NDv2-V100 nodes. The goal was to evaluate the efficiency of fine-tuning across different numbers of nodes (1, 2, and 3) and observe the impact on training time and throughput. Azure ML Job YAML Definition $schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json type: command code: ./ # Path to your training script and related files inputs: model_dir: path: azureml://registries/azureml/models/mistralai-Mistral-7B-v01/versions/19 command: > accelerate launch --num_processes 16 # gpu per machine * num of machines --num_machines 2 --machine_rank $NODE_RANK --main_process_ip $MASTER_ADDR --main_process_port $MASTER_PORT compute: azureml:ndv2-cluster resources: instance_count: 2 # Number of nodes for distributed training distribution: type: pytorch process_count_per_instance: 1 # Number of processes per node Results: As you increased the number of nodes from one to three, the throughput increased proportionally. This indicates that the system scaled efficiently with the addition of more nodes, maintaining a close-to-linear improvement in throughput. Experiment 2: Model Parallelism using FSDP Fine-tuning a 70B-parameter model on GPUs with only 16GB of memory might sound impossible, but we made it happen using FSDP (Full Sharded Data Parallelism) on Azure ML using a cluster of multiple NDv2-V100 nodes. By distributing not only the data but also the model parameters and optimizer states across multiple nodes, we unlocked the power of full sharding. $schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json type: command code: ./ # Path to your training script and related files inputs: model_dir: path: azureml://registries/azureml-meta/models/Llama-3.3-70B-Instruct/versions/4 command: > accelerate launch --config_file "configs/fsdp_config.yaml" --num_processes 32 --num_machines 4 --machine_rank $NODE_RANK --main_process_ip $MASTER_ADDR --main_process_port $MASTER_PORT train.py compute: azureml:ndv2-cluster resources: instance_count: 4 # Number of nodes for distributed training distribution: type: pytorch process_count_per_instance: 1 # Number of processes per node Key Takeaways: Memory Efficiency: Full sharding enabled us to fine-tune the LLaMA-70B model on V100 GPUs despite their limited memory. Connectivity Matters: The Infiniband (IB) connectivity of ND nodes played a critical role in ensuring smooth communication across GPUs, making this feat possible. Conclusion Scalable and efficient fine-tuning is the key to unlocking the true potential of Large Language Models. By leveraging distributed training techniques, such as FSDP and DDP, and optimizing compute resources on Azure ML, researchers and practitioners can overcome the challenges of training massive models—reducing costs, accelerating time-to-value, and driving AI innovation. Access the code and start experimenting here! Future work: The second part will focus on real-world pipeline setups, including end-to-end model training, hyperparameter optimization, and testing. The third part will dive into deploying trained models for practical use. Future posts may explore best practices for specific fine-tuning scenarios and techniques.
JamesN
Apr 14, 2025 Place AI - Machine Learning Blog
1.7KViews
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AI Avatars: Redefining Human-Digital Interaction in the Enterprise Era
In today’s AI-driven world, businesses are constantly seeking innovative ways to humanize digital experiences. AI Avatars are emerging as a powerful solution—bridging the gap between intelligent automation and authentic, human-like engagement. With advancements in speech synthesis, large language models, and avatar rendering technologies, organizations can now deploy AI-powered digital assistants that not only understand and respond but also interact with a lifelike presence. The Rise of AI Avatars in Enterprise Applications AI Avatars go beyond traditional chatbots or voice assistants. These virtual beings offer multimodal interaction—combining voice, visual cues, and conversational intelligence into a seamless user experience. Built on enterprise-grade platforms like Azure AI, these avatars can be integrated into customer support portals, digital kiosks, internal knowledge hubs, and more. Their utility spans a range of industries: Retail: Personalized shopping assistants that guide consumers through products. Healthcare: Virtual health concierges that help patients navigate care. Education: Interactive tutors that deliver lessons with empathy and responsiveness. HR and Training: Onboarding avatars that answer employee questions, onboard new hires, or provide compliance updates. One of our key partners, Cloudforce, has integrated AI Avatar technology directly into their flagship platform nebulaONE®. This integration enables enterprises to deploy digital assistants that are deeply embedded in business processes, offering contextualized support and real-time engagement. From training and onboarding to employee self-service, nebulaONE's agentic AI Avatars act as a digital bridge between users and systems—driving efficiency, engagement, and satisfaction. Partner Spotlight: Cloudforce’s Avatar Initiative To operationalize and productize AI Avatars, Microsoft collaborates with a growing ecosystem of partners. Cloudforce is one of the early pioneers in this space. Their work in embedding avatars into nebulaONE demonstrates what’s possible when advanced AI meets real-world enterprise needs. With a vision to transform user interaction across industries, Cloudforce built a production-grade AI Avatar module designed to support customer Q&A, knowledge discovery, and live guided walkthroughs. Leveraging Azure OpenAI, Azure AI Speech, and privately-deployed secure cloud infrastructure, they have brought conversational intelligence to life—with both a face and a voice. Looking ahead, Cloudforce’s broader vision is to bring AI Avatar capabilities to millions of students—delivering immersive learning experiences that blend interactivity, personalization, and scale. Their education-focused roadmap enhancements highlight the potential of avatars not just as productivity agents, but as accessible and empathetic digital educators, delivering equitable access to knowledge previously reserved for a fortunate few. This kind of partner innovation illustrates how AI Avatars can be customized and scaled to deliver tangible business value across multiple domains. Partner Contribution "Students are already embracing generative AI at a pace and proficiency that far exceeds many professional audiences. With Azure's AI Avatar technology, educators and institutions can tailor unique GenAI interactions that promote reasoning and learning over simply receiving answers the way they would with common public bots." says Husein Sharaf, Founder and CEO at Cloudforce. "We understand the concerns and hesitation that our education partners are currently grappling with, however we believe they can and should take an active role in shaping how this transformative technology is leveraged across their campuses, or risk being left behind as students choose their own adventure." "Microsoft's enterprise AI capabilities are enabling partners like us to deliver secure, cost-efficient, and responsible AI experiences at scale. With the Azure AI Foundry and key innovations like AI Avatars as our building blocks, the nebulaONE platform is poised to serve as the GenAI gateway to tens of thousands of business users, and millions of students at leading educational institutions globally. Our customers are seeking unique differentiators that will enable them to compete and win in the age of AI, and our collaboration with Microsoft is empowering us to deliver just that." Summary AI Avatars represent the next frontier in digital interaction. By combining conversational AI, expressive voice synthesis, and realistic visual rendering, these intelligent agents deliver truly human-like experiences—at scale. They are not just tools, but digital extensions of your brand. Partners like Cloudforce are leading the way with innovative platforms like nebulaONE, showing how this technology can be embedded into enterprise solutions and educational experiences to drive efficiency with a human touch. While Cloudforce is among the first to productize AI Avatars using Azure AI, they are part of a growing movement—helping to shape the future of AI-powered experiences across industries. As AI continues to evolve, avatars will become a standard interface—transforming the way we learn, work, and engage with digital systems.
Gana_Chandrasekaran
Apr 08, 2025 Place AI - AI Platform Blog
1.2KViews
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2Comments
March 2024 Recap: Azure Database for PostgreSQL Flexible Server
March 2024 Feature Recap: Azure PostgreSQL Flexible Server - New Features and Enhancements
varun-dhawan
Apr 08, 2025 Place Azure Database for PostgreSQL Blog
8.9KViews
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Serverless Fine Tuning Now In More US-Regions!
We value your feedback and recognize the demand for fine tuning to be accessible in more regions. Today, we are excited to announce that serverless finetuning for Mistral, Phi, and NTT models is now available across all US regions where base model inferencing is also accessible. This expansion aims to provide greater flexibility and accessibility for users, ensuring that everyone can benefit from the enhanced capabilities of serverless finetuning. Region Availability Cross region finetuning is now enabled in the following regions: EastUS EastUS2 SouthCentralUS NorthCentralUS WestUS WestUS3 Model Availability Mistral-Nemo Mistral-Large-2411 Ministral-3B Phi-3.5-mini-instruct Phi-3.5-MoE-instruct Phi-4-mini-instruct Tsuzumi-7b Looking Ahead: More Models and Regions As we continue to innovate and expand our model offerings, more models and regions will soon be supported. Our team is working diligently to ensure that users across various locations can benefit from the latest advancements in serverless finetuning. Stay tuned for updates as we roll out these enhancements, providing even greater flexibility and accessibility for our global user base. We appreciate your ongoing support and look forward to sharing more details in the near future. Get started today! Whether you're a newcomer to fine-tuning or an experienced developer, getting started with Azure AI Foundry is now more accessible than ever. Fine-tuning is available through both Azure AI Foundry and Azure ML Studio, offering a user-friendly interface for those who prefer a graphical user interface (GUI) and SDK’s and CLI for advanced users. Learn more! Try it out with Azure AI Foundry Explore documentation for the model catalog in Azure AI Foundry Begin using the Finetuning SDK in the notebook Learn more about Azure AI Content Safety - Azure AI Content Safety – AI Content Moderation | Microsoft Azure Get started with Finetuning on Azure AI Foundry Learn more about region availability
RashaudSavage
Apr 04, 2025 Place AI - Machine Learning Blog
214Views
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Power BI & Azure Databricks: Smarter Refreshes, Less Hassle
We are excited to extend the deep integration between Azure Databricks and Microsoft Power BI with the Public Preview of the Power BI task type in Azure Databricks Workflows. This new capability allows users to update and refresh Power BI semantic models directly from their Azure Databricks workflows, ensuring real-time data updates for reports and dashboards. By leveraging orchestration and triggers within Azure Databricks Workflows, organizations can improve efficiency, reduce refresh costs, and enhance data accuracy for Power BI users. Power BI tasks seamlessly integrate with Unity Catalog in Azure Databricks, enabling automated updates to tables, views, materialized views, and streaming tables across multiple schemas and catalogs. With support for Import, DirectQuery, and Dual Storage modes, Power BI tasks provide flexibility in managing performance and security. This direct integration eliminates manual processes, ensuring Power BI models stay synchronized with underlying data without requiring context switching between platforms. Built into Azure Databricks Lakeflow, Power BI tasks benefit from enterprise-grade orchestration and monitoring, including task dependencies, scheduling, retries, and notifications. This streamlines workflows and improves governance by utilizing Microsoft Entra ID authentication and Unity Catalog suite of security and governance offerings. We invite you to explore the new Power BI tasks today and experience seamless data integration—get started by visiting the [ADB Power BI task documentation].
LindseyAllen
Apr 01, 2025 Place Analytics on Azure Blog
1.4KViews
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The Future of AI: Harnessing AI for E-commerce - personalized shopping agents
Explore the development of personalized shopping agents that enhance user experience by providing tailored product recommendations based on uploaded images. Leveraging Azure AI Foundry, these agents analyze images for apparel recognition and generate intelligent product recommendations, creating a seamless and intuitive shopping experience for retail customers.
manniarora
Apr 01, 2025 Place AI - AI Platform Blog
709Views
5likes
3Comments
Anthropic State-of-the-Art Models Available to Azure Databricks Customers
Our customers now have greater model choices with the arrival of Anthropic Claude 3.7 Sonnet in Azure Databricks. Databricks is announcing a partnership with Anthropic to integrate their state-of-the-art models into Databricks Data Intelligence Platform as a native offering, starting with Claude 3.7 Sonnet http://databricks.com/blog/anthropic-claude-37-sonnet-now-natively-available-databricks. With this announcement, Azure customers can use Claude Models directly in Azure Databricks. Foundation model REST API reference - Azure Databricks | Microsoft Learn With Anthropic models available in Azure Databricks, customers can use the Claude "think" tool with business data optimized promote to guide Claude efficiently perform complex tasks. With Claude models in Azure Databricks, enterprises can deliver domain-specific, high quality AI agents more efficiently. As an integrated component of the Azure Databricks Data Intelligence Platform, Anthropic Claude models benefit from comprehensive end-to-end governance and monitoring throughout the entire data and AI lifecycle with Unity Catalog. With Claude models, we remain committed to providing customers with model flexibility. Through the Azure Databricks Data Intelligence Platform, customers can securely connect to any model provider and select the most suitable model for their needs. They can further enhance these models with enterprise data to develop domain-specific, high-quality AI agents, supported by built-in custom evaluation governance across both data and models.
LindseyAllen
Mar 27, 2025 Place Analytics on Azure Blog
3.5KViews
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Developer Blog","shortTitle":"Educator Developer Blog","parent":{"__ref":"Category:category:EducationSector"}},"User:user:2181140":{"__typename":"User","uid":2181140,"login":"ShivamGoyal03","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS0yMTgxMTQwLTU2NDU5OWlENTNEOTUwNTkyNDMwODVE"},"id":"user:2181140"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAyMjUzLWNvYUpPWg?revision=2\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAyMjUzLWNvYUpPWg?revision=2","title":"importance-of-metacognition.png","associationType":"COVER","width":1920,"height":1080,"altText":""},"BlogTopicMessage:message:4402253":{"__typename":"BlogTopicMessage","subject":"AI Agents: Metacognition for Self-Aware Intelligence - Part 9","conversation":{"__ref":"Conversation:conversation:4402253"},"id":"message:4402253","revisionNum":2,"uid":4402253,"depth":0,"board":{"__ref":"Blog:board:EducatorDeveloperBlog"},"author":{"__ref":"User:user:2181140"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" This blog post, Part 9 in a series on AI agents, introduces the concept of metacognition, or \"thinking about thinking,\" and its application to AI agents. It explains how metacognition enables agents to self-evaluate, adapt, and improve their performance. The post outlines the key components of an AI agent and illustrates metacognition with a travel agent example, demonstrating how it can enhance planning, error correction, and personalization. The post also discusses the Corrective RAG approach and demonstrates code snippets. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":103},"postTime":"2025-04-28T00:00:00.010-07:00","lastPublishTime":"2025-04-28T00:00:00.010-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Hi everyone, Shivam Goyal here! This blog series, based on Microsoft's AI Agents for Beginners repository, continues with an exciting topic: Metacognition in AI Agents. In previous posts ([links to parts 1-8 at the end]), we've covered fundamental concepts and design patterns. Now, we'll explore how to equip AI agents with the ability to \"think about thinking,\" enabling them to evaluate, adapt, and improve their own cognitive processes. \n What is Metacognition? \n Metacognition, often described as \"thinking about thinking,\" refers to higher-order cognitive processes that involve self-awareness and self-regulation of one's cognitive activities. In AI, this means enabling agents to evaluate their actions, identify errors, and adjust strategies based on past experiences. This self-awareness allows AI agents to make better decisions, adapt to new situations, and improve performance over time. \n Why is Metacognition Important for AI Agents? \n Metacognition is crucial for: \n \n Self-Reflection: Agents can assess their performance and pinpoint areas for improvement. \n Adaptability: They can modify strategies based on experiences and changing environments. \n Error Correction: Agents can detect and correct errors autonomously. \n Resource Management: They can optimize resource usage (time, computation) through planning and evaluation. \n \n Key Components of an AI Agent with Metacognition \n An AI agent incorporating metacognition builds upon these basic components: \n \n Persona: Defines the agent's personality and interaction style. \n Tools: Represents the agent's capabilities and functions. \n Skills: Encompasses the agent's knowledge and expertise. \n \n These components work together to form an \"expertise unit,\" but metacognition adds a layer of self-awareness and adaptability. \n Metacognition in Action: A Travel Agent Example \n Consider a \"Travel Agent\" AI assisting users with vacation planning. It can leverage metacognition to provide more personalized recommendations by: \n \n Analyzing user feedback to determine what was well-received and what wasn't. \n Adapting to user preferences (e.g., avoiding crowded places if a user previously expressed a dislike for them). \n Correcting past errors (e.g., rigorously checking hotel availability after previously recommending a fully booked hotel). \n \n Planning in Agents \n Planning is a key aspect of AI agent behavior, involving outlining the steps required to achieve a goal while considering the current state, resources, and potential obstacles. The Travel Agent follows these steps: \n \n Gather User Preferences \n Retrieve Information (flights, hotels, attractions) \n Generate Recommendations \n Present Itinerary to User \n Collect Feedback \n Adjust Based on Feedback \n Final Confirmation \n Book and Confirm Reservations \n Provide Ongoing Support \n \n Corrective RAG Approach \n Corrective Retrieval-Augmented Generation (RAG) focuses on correcting errors and improving accuracy. This involves: \n \n Prompting Technique: Using specific prompts to guide information retrieval. \n Tool: Implementing algorithms to evaluate the relevance of retrieved information. \n Evaluation: Continuously assessing the agent's performance and making adjustments. \n \n Compared to Pre-emptive Context Load, which is what happens if we load travel destinations into the agent system. \n Demonstrating Metacognition in Code \n To demonstrate metacognition, let's create a simple agent that reflects on its decision-making process. We'll use a hotel recommendation agent as an example. It starts with the goal of picking cheapest hotel but the result aren't of good quality, so it uses best quality instead. \n The key aspects of this code illustrating metacognition are: the ability to evaluate previous choices, and adjust it's strategies as a result. \n Summary and Further Exploration \n Metacognition is a powerful tool for enhancing AI agents, making them more intelligent, adaptable, and efficient. Explore the provided code examples and additional resources to further delve into this fascinating area. \n Further Resources and Learning \n \n ai-agents-for-beginners/09-metacognition at main · microsoft/ai-agents-for-beginners \n AI Agents for Beginners Repository \n \n Catch up on the series: \n \n Part 1: Introduction to AI Agents \n Part 2: Exploring Agentic Frameworks \n Part 3: Agentic Design Principles \n Part 4: Tool Use Design Pattern \n Part 5: AI Agents: Mastering Agentic RAG \n Part 6: Building Trustworthy Agents \n Part 7: Planning and Orchestration with the Planning Design Pattern \n Part 8: Multi-Agent Design Pattern \n \n If you have any further questions or would like to connect for more discussion, feel free to reach out to me on and continue your learning and discussions on the Microsoft Azure AI Foundry Discord. ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"4838","kudosSumWeight":0,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAyMjUzLWNvYUpPWg?revision=2\"}"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":{"__typename":"UploadedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAyMjUzLWNvYUpPWg?revision=2"},"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4403795":{"__typename":"Conversation","id":"conversation:4403795","topic":{"__typename":"ForumTopicMessage","uid":4403795},"lastPostingActivityTime":"2025-04-20T19:12:17.974-07:00","solved":false},"Forum:board:MachineLearning":{"__typename":"Forum","id":"board:MachineLearning","displayId":"MachineLearning","nodeType":"board","conversationStyle":"FORUM","title":"AI - Machine Learning","shortTitle":"AI - Machine Learning","parent":{"__ref":"Category:category:AI"}},"User:user:1283329":{"__typename":"User","uid":1283329,"login":"dbaechtel","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/m_assets/avatars/default/avatar-9.svg?time=0"},"id":"user:1283329"},"ForumTopicMessage:message:4403795":{"__typename":"ForumTopicMessage","subject":"Using Neural Network to Learn Profitable Trading in the FOREX Markets","conversation":{"__ref":"Conversation:conversation:4403795"},"id":"message:4403795","revisionNum":1,"uid":4403795,"depth":0,"board":{"__ref":"Forum:board:MachineLearning"},"author":{"__ref":"User:user:1283329"},"metrics":{"__typename":"MessageMetrics","views":55},"postTime":"2025-04-13T07:58:12.971-07:00","lastPublishTime":"2025-04-13T07:58:12.971-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" I am using Neural Networks (NN) to teach them how to recognize profitable trading opportunities in the Foreign Exchange (FOREX) markets, using 10 currencies simultaneously. I am using 3rd-order Cubic Splines as input to give the NNs a sense of how the critical variables change over time. I am using free FOREX historical trading data to train the NNs how to trade profitably in the future. I don't just feed the trading levels of the FOREX currency pairs as input to the NNs. Instead, I use a variation of the computed DXY Index for all 10 currencies in order to isolate the value change of each of the individual currencies, using Cubic Splines to detail how those values change over various time periods. The end result is Neural Networks that recognize which currencies to Buy and which ones to Sell at the most profitable times. If anyone is interested in the details, please reach out and I will provide more details. ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"938","kudosSumWeight":1,"repliesCount":3,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}}},"Conversation:conversation:4403609":{"__typename":"Conversation","id":"conversation:4403609","topic":{"__typename":"BlogTopicMessage","uid":4403609},"lastPostingActivityTime":"2025-04-14T10:02:23.510-07:00","solved":false},"Blog:board:AIPlatformBlog":{"__typename":"Blog","id":"board:AIPlatformBlog","displayId":"AIPlatformBlog","nodeType":"board","conversationStyle":"BLOG","title":"AI - AI Platform Blog","shortTitle":"AI - AI Platform Blog","parent":{"__ref":"Category:category:AI"}},"User:user:55564":{"__typename":"User","uid":55564,"login":"Naomi Moneypenny","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS01NTU2NC1ONjRSQTE?image-coordinates=0%2C0%2C400%2C400"},"id":"user:55564"},"BlogTopicMessage:message:4403609":{"__typename":"BlogTopicMessage","subject":"Expanding the Llama 4 Herd: New Models Now Available on Azure AI Foundry","conversation":{"__ref":"Conversation:conversation:4403609"},"id":"message:4403609","revisionNum":4,"uid":4403609,"depth":0,"board":{"__ref":"Blog:board:AIPlatformBlog"},"author":{"__ref":"User:user:55564"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"Releasing as Serverless offering today, Llama 4 Scout and Maverick Instruct models. Also available on GitHub Models.","metrics":{"__typename":"MessageMetrics","views":352},"postTime":"2025-04-14T10:02:23.510-07:00","lastPublishTime":"2025-04-14T10:02:23.510-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Last week, we kicked off the arrival of Meta’s powerful new Llama 4 models in Azure with the launch of three models across Azure AI Foundry and Azure Databricks. Today, we’re expanding the herd with the addition of two new 17B-parameter instruction-tuned models — now available in the Azure AI Foundry model catalog as Models as a Service (MaaS) endpoints \n New Models \n \n Llama-4-Scout-17B-16E-Instruct A fast, low-latency 17B model with 16 experts — optimized for general-purpose tasks with strong instruction following. \n Llama-4-Maverick-17B-128E-Instruct-FP8 A larger, more expressive variant with 128 experts and FP8 precision — built for heavier, higher-quality reasoning under constrained compute. \n \n Both models are: \n \n Hosted as serverless MaaS endpoints — no infrastructure setup required \n Available on GitHub Models and playground \n \n What Makes These Llama 4 Models Special? \n These models are part of Meta’s mixture-of-experts (MoE) family of Llama 4 variants. Unlike dense models, these MoE architectures selectively activate a subset of model parameters (experts) per token, yielding improved efficiency without sacrificing output quality. \n \n Scout-17B-16E offers fast inference for common enterprise workloads like summarization, Q&A, and structured output tasks. \n Maverick-17B-128E-FP8 introduces aggressive expert scaling and FP8 precision, enabling high-throughput inference with improved energy efficiency. \n \n How to Get Started \n You can find these models in the Azure AI Foundry model catalog — just search for \"Llama-4\" or navigate to the Meta model family. With a few clicks, you can: \n \n Deploy the model as a serverless endpoint \n Invoke it via the Azure AI Foundry playground \n Integrate using the Azure OpenAI-compatible REST API or Python SDK \n \n Use Cases \n These 17B models are a great fit for: \n \n Knowledge assistant copilots \n Long-form summarization \n Table-to-text transformation \n Conversational agents \n Internal developer tools \n \n Explore More \n To learn more about last week’s launch of Llama 4 models, including 8B and 70B variants, check out the official Azure blog: Introducing the Llama 4 Herd in Azure AI Foundry and Azure Databricks \n Try these models today in Azure AI Foundry and let us know what you build! \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"2286","kudosSumWeight":0,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4362787":{"__typename":"Conversation","id":"conversation:4362787","topic":{"__typename":"BlogTopicMessage","uid":4362787},"lastPostingActivityTime":"2025-04-14T07:27:09.722-07:00","solved":false},"Blog:board:MachineLearningBlog":{"__typename":"Blog","id":"board:MachineLearningBlog","displayId":"MachineLearningBlog","nodeType":"board","conversationStyle":"BLOG","title":"AI - Machine Learning Blog","shortTitle":"AI - Machine Learning Blog","parent":{"__ref":"Category:category:AI"}},"User:user:821337":{"__typename":"User","uid":821337,"login":"JamesN","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS04MjEzMzctMzM2NjIxaTU3OTlDOUM1ODU1NTE1Q0Q"},"id":"user:821337"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYyNzg3LUlnY0plWA?revision=32\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYyNzg3LUlnY0plWA?revision=32","title":"job_pipeline.png","associationType":"COVER","width":1450,"height":865,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYyNzg3LVVVcUVyZg?revision=32\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYyNzg3LVVVcUVyZg?revision=32","title":"clipboard_image-1-1737689434154.png","associationType":"BODY","width":1189,"height":590,"altText":""},"BlogTopicMessage:message:4362787":{"__typename":"BlogTopicMessage","subject":"Scalable and Efficient Fine-Tuning of LLM on Azure ML","conversation":{"__ref":"Conversation:conversation:4362787"},"id":"message:4362787","revisionNum":32,"uid":4362787,"depth":0,"board":{"__ref":"Blog:board:MachineLearningBlog"},"author":{"__ref":"User:user:821337"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"\nAs Large Language Models (LLMs) continue to revolutionize industries with their ever-expanding capabilities, fine-tuning them efficiently and at scale has become a critical challenge. Fine-tuning is how we adapt these general-purpose models to excel at specific tasks—whether it’s summarizing legal documents, generating creative copy, or analyzing sentiment in customer reviews. However, the massive size and computational demands of LLMs make fine-tuning no small feat.\n\nIn this blog, we’ll dive into strategies for scalable fine-tuning on Azure Machine Learning (Azure ML), share experimental results that demonstrate the power of distributed training.\n\nOh, and we’ve got some exciting experiments to share:\n1- 3x faster fine-tuning by scaling from 1 to 3 machines.\n2 - Successfully fine-tuned a 70B-parameter model using old V100 GPUs—a feat made possible by efficient distributed training techniques.\n\n\nCurious to try it yourself? ","metrics":{"__typename":"MessageMetrics","views":1723},"postTime":"2025-01-23T15:11:59.720-08:00","lastPublishTime":"2025-04-14T07:27:09.722-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" https://github.com/james-tn/llm-fine-tuning/tree/main/opensource_llm/single_step \n Co-Author: Mohamad AL jazaery Why Scalable and Efficient Fine-Tuning Matters \n Faster Iterations, Shorter Time-to-Value: \n In today’s competitive AI landscape, time is of the essence. The faster you can fine-tune a model, the quicker you can validate ideas, test hypotheses, and bring solutions to market. \n High-profile GPU machines are costly: \n High-performance GPUs and compute clusters don’t come cheap, and their availability is often limited. Efficient fine-tuning techniques, such as model sharding and distributed training, maximize the utilization of these precious resources—ensuring that you get the most out of your infrastructure investment. \n \n Choosing the Right Azure ML GPU Compute for the Job: NC or ND? \n Not all GPU computes are created equal, and choosing the right sku can make or break your training efficiency. \n \n ND Series: Ideal for distributed training across multiple nodes, thanks to its Infiniband (IB) connectivity that ensures high-speed communication between nodes like pretraining LLM or finetuning very large model ~70B params. \n NC Series: Small and medium workload where no heavy interaction between nodes needed like LLM inferencing or mid-size LLM finetuning. \n \n Azure GPU Machine Options by Scenario: \n \n Scenario \n \n Common model size \n \n Training Approach \n \n Recommended Azure Compute \n \n Small-scale fine-tuning \n \n < 3B parameters \n \n Parameter-efficient tuning \n \n NCas_T4_v3 (Tesla T4, 16 GB) \n \n Medium-scale fine-tuning \n \n 1–5B parameters \n \n Full or parameter-efficient \n \n NCs_v3 (Tesla V100, 16 GB) \n \n Distributed training for medium models \n \n 5–10B parameters \n \n Full fine-tuning \n \n ND_v2 (Tesla V100 NVLINK, 32 GB, InfiniBand) \n \n Large-scale fine-tuning (single machine) \n \n 10–30B parameters \n \n Full or parameter-efficient \n \n NC_A100_v4 (A100, 40 GB) \n \n Distributed training for very large models \n \n 20–70B parameters \n \n Full fine-tuning \n \n NDasrA100_v4 (A100, 80 GB, HDR InfiniBand) \n \n Very large models training (single machine) \n \n up to 70B parameters \n \n Full or parameter-efficient \n \n NCads_H100_v5 (H100 NVL, 94 GB) \n \n Massive-scale distributed training \n \n > 70B parameters \n \n Full fine-tuning \n \n ND-H100-v5 (H100, 80 GB, scale-out InfiniBand) \n \n \n \n Distributed Efficient Training: A Quick Guide \n When scaling fine-tuning tasks, choosing the right distributed training method is key: \n \n DDP (Data Parallelism): Works well when the entire model fits on a single GPU. It replicates the model across multiple GPUs and splits the data for parallel processing. Check experiment 1 in the following section. \n Model Parallelism: A game-changer for massive models that don’t fit on a single GPU. It shards not only the data but also the model parameters and optimizer states across multiple GPUs, enabling efficient training of models like LLaMA-70B on GPUs with low memory GPUs. Both FSDP and DeepSpeed as libraries excel at implementing advanced forms of model parallelism and memory optimization. \n Memory Optimization Techniques\n \n Gradient Checkpointing: Reduces memory by recomputing activations during the backward pass, trading memory for additional computation. \n \n \n \n \n \n \n Mixed Precision Training: Reduces memory usage by using FP16 or BF16 instead of FP32, accelerating training while maintaining numerical stability. Supported by both frameworks. \n Quantization (DeepSpeed Exclusive): Uses INT8 precision for weights and activations, dramatically reducing memory and compute requirements. \n Offloading (DeepSpeed Exclusive): Offloads optimizer states and model parameters to CPU or NVMe, freeing up GPU memory for computation. \n \n \n \n \n \n Our Experiments: Pushing the Limits of Scalability \n Experiment 1: Distributed Training on Multiple Nodes using DDP \n We conducted an experiment to fine-tune the Llama-3.1-8B model using LoRA (Low-Rank Adaptation) on Azure ML NDv2-V100 nodes. The goal was to evaluate the efficiency of fine-tuning across different numbers of nodes (1, 2, and 3) and observe the impact on training time and throughput. \n Azure ML Job YAML Definition \n $schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json type: command code: ./ # Path to your training script and related files inputs: model_dir: path: azureml://registries/azureml/models/mistralai-Mistral-7B-v01/versions/19 command: > accelerate launch --num_processes 16 # gpu per machine * num of machines --num_machines 2 --machine_rank $NODE_RANK --main_process_ip $MASTER_ADDR --main_process_port $MASTER_PORT compute: azureml:ndv2-cluster resources: instance_count: 2 # Number of nodes for distributed training distribution: type: pytorch process_count_per_instance: 1 # Number of processes per node \n \n Results: \n \n As you increased the number of nodes from one to three, the throughput increased proportionally. This indicates that the system scaled efficiently with the addition of more nodes, maintaining a close-to-linear improvement in throughput. \n \n Experiment 2: Model Parallelism using FSDP \n Fine-tuning a 70B-parameter model on GPUs with only 16GB of memory might sound impossible, but we made it happen using FSDP (Full Sharded Data Parallelism) on Azure ML using a cluster of multiple NDv2-V100 nodes. By distributing not only the data but also the model parameters and optimizer states across multiple nodes, we unlocked the power of full sharding. \n $schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json type: command code: ./ # Path to your training script and related files inputs: model_dir: path: azureml://registries/azureml-meta/models/Llama-3.3-70B-Instruct/versions/4 command: > accelerate launch --config_file \"configs/fsdp_config.yaml\" --num_processes 32 --num_machines 4 --machine_rank $NODE_RANK --main_process_ip $MASTER_ADDR --main_process_port $MASTER_PORT train.py compute: azureml:ndv2-cluster resources: instance_count: 4 # Number of nodes for distributed training distribution: type: pytorch process_count_per_instance: 1 # Number of processes per node \n Key Takeaways: \n \n Memory Efficiency: Full sharding enabled us to fine-tune the LLaMA-70B model on V100 GPUs despite their limited memory. \n Connectivity Matters: The Infiniband (IB) connectivity of ND nodes played a critical role in ensuring smooth communication across GPUs, making this feat possible. \n \n \n Conclusion \n Scalable and efficient fine-tuning is the key to unlocking the true potential of Large Language Models. By leveraging distributed training techniques, such as FSDP and DDP, and optimizing compute resources on Azure ML, researchers and practitioners can overcome the challenges of training massive models—reducing costs, accelerating time-to-value, and driving AI innovation. \n Access the code and start experimenting here! \n \n Future work: The second part will focus on real-world pipeline setups, including end-to-end model training, hyperparameter optimization, and testing. The third part will dive into deploying trained models for practical use. Future posts may explore best practices for specific fine-tuning scenarios and techniques. ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"7361","kudosSumWeight":3,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYyNzg3LUlnY0plWA?revision=32\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYyNzg3LVVVcUVyZg?revision=32\"}"}}],"totalCount":2,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":{"__typename":"UploadedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYyNzg3LUlnY0plWA?revision=32"},"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4401973":{"__typename":"Conversation","id":"conversation:4401973","topic":{"__typename":"BlogTopicMessage","uid":4401973},"lastPostingActivityTime":"2025-04-08T13:36:19.243-07:00","solved":false},"User:user:2424112":{"__typename":"User","uid":2424112,"login":"Gana_Chandrasekaran","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS0yNDI0MTEyLTU3MTY1NGlBMjU4NzkyNDk3OUZBOEJB"},"id":"user:2424112"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxOTczLUVia0lKQw?revision=4\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxOTczLUVia0lKQw?revision=4","title":"image.png","associationType":"COVER","width":2040,"height":1536,"altText":""},"BlogTopicMessage:message:4401973":{"__typename":"BlogTopicMessage","subject":"AI Avatars: Redefining Human-Digital Interaction in the Enterprise Era","conversation":{"__ref":"Conversation:conversation:4401973"},"id":"message:4401973","revisionNum":4,"uid":4401973,"depth":0,"board":{"__ref":"Blog:board:AIPlatformBlog"},"author":{"__ref":"User:user:2424112"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":1153},"postTime":"2025-04-08T06:02:35.548-07:00","lastPublishTime":"2025-04-08T06:48:47.198-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" In today’s AI-driven world, businesses are constantly seeking innovative ways to humanize digital experiences. AI Avatars are emerging as a powerful solution—bridging the gap between intelligent automation and authentic, human-like engagement. With advancements in speech synthesis, large language models, and avatar rendering technologies, organizations can now deploy AI-powered digital assistants that not only understand and respond but also interact with a lifelike presence. \n \n The Rise of AI Avatars in Enterprise Applications \n AI Avatars go beyond traditional chatbots or voice assistants. These virtual beings offer multimodal interaction—combining voice, visual cues, and conversational intelligence into a seamless user experience. Built on enterprise-grade platforms like Azure AI, these avatars can be integrated into customer support portals, digital kiosks, internal knowledge hubs, and more. \n Their utility spans a range of industries: \n \n Retail: Personalized shopping assistants that guide consumers through products. \n Healthcare: Virtual health concierges that help patients navigate care. \n Education: Interactive tutors that deliver lessons with empathy and responsiveness. \n HR and Training: Onboarding avatars that answer employee questions, onboard new hires, or provide compliance updates. \n \n One of our key partners, Cloudforce, has integrated AI Avatar technology directly into their flagship platform nebulaONE®. This integration enables enterprises to deploy digital assistants that are deeply embedded in business processes, offering contextualized support and real-time engagement. From training and onboarding to employee self-service, nebulaONE's agentic AI Avatars act as a digital bridge between users and systems—driving efficiency, engagement, and satisfaction. \n \n Partner Spotlight: Cloudforce’s Avatar Initiative \n To operationalize and productize AI Avatars, Microsoft collaborates with a growing ecosystem of partners. Cloudforce is one of the early pioneers in this space. Their work in embedding avatars into nebulaONE demonstrates what’s possible when advanced AI meets real-world enterprise needs. \n With a vision to transform user interaction across industries, Cloudforce built a production-grade AI Avatar module designed to support customer Q&A, knowledge discovery, and live guided walkthroughs. Leveraging Azure OpenAI, Azure AI Speech, and privately-deployed secure cloud infrastructure, they have brought conversational intelligence to life—with both a face and a voice. \n Looking ahead, Cloudforce’s broader vision is to bring AI Avatar capabilities to millions of students—delivering immersive learning experiences that blend interactivity, personalization, and scale. Their education-focused roadmap enhancements highlight the potential of avatars not just as productivity agents, but as accessible and empathetic digital educators, delivering equitable access to knowledge previously reserved for a fortunate few. \n This kind of partner innovation illustrates how AI Avatars can be customized and scaled to deliver tangible business value across multiple domains. \n \n Partner Contribution \n \"Students are already embracing generative AI at a pace and proficiency that far exceeds many professional audiences. With Azure's AI Avatar technology, educators and institutions can tailor unique GenAI interactions that promote reasoning and learning over simply receiving answers the way they would with common public bots.\" says Husein Sharaf, Founder and CEO at Cloudforce. \"We understand the concerns and hesitation that our education partners are currently grappling with, however we believe they can and should take an active role in shaping how this transformative technology is leveraged across their campuses, or risk being left behind as students choose their own adventure.\" \n \"Microsoft's enterprise AI capabilities are enabling partners like us to deliver secure, cost-efficient, and responsible AI experiences at scale. With the Azure AI Foundry and key innovations like AI Avatars as our building blocks, the nebulaONE platform is poised to serve as the GenAI gateway to tens of thousands of business users, and millions of students at leading educational institutions globally. Our customers are seeking unique differentiators that will enable them to compete and win in the age of AI, and our collaboration with Microsoft is empowering us to deliver just that.\" \n \n Summary \n AI Avatars represent the next frontier in digital interaction. By combining conversational AI, expressive voice synthesis, and realistic visual rendering, these intelligent agents deliver truly human-like experiences—at scale. They are not just tools, but digital extensions of your brand. \n Partners like Cloudforce are leading the way with innovative platforms like nebulaONE, showing how this technology can be embedded into enterprise solutions and educational experiences to drive efficiency with a human touch. While Cloudforce is among the first to productize AI Avatars using Azure AI, they are part of a growing movement—helping to shape the future of AI-powered experiences across industries. \n As AI continues to evolve, avatars will become a standard interface—transforming the way we learn, work, and engage with digital systems. 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2024 Recap: Azure Database for PostgreSQL Flexible Server","conversation":{"__ref":"Conversation:conversation:4107275"},"id":"message:4107275","revisionNum":49,"uid":4107275,"depth":0,"board":{"__ref":"Blog:board:ADforPostgreSQL"},"author":{"__ref":"User:user:1499312"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" March 2024 Feature Recap: Azure PostgreSQL Flexible Server - New Features and Enhancements \n ","introduction":"","metrics":{"__typename":"MessageMetrics","views":8939},"postTime":"2024-04-09T10:09:57.905-07:00","lastPublishTime":"2025-04-07T19:07:24.797-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Welcome, Azure Community! \n March has been an exciting month for Azure Database for PostgreSQL Flexible Server, as we introduced many new features and improvements that greatly enhance how databases are managed and perform on Azure. Following a tradition of innovation and improvement, this month's updates bring even more capabilities to your database deployments, ensuring that your applications are more secure, efficient, and scalable. Let's explore the notable features that were launched in March 2024. \n \n Highlights of the Month \n \n Migration Service in Azure Database for PostgreSQL (General Availability) \n Support for the latest Postgres minor versions: 16.2, 15.6, 14.11, 13.14, and 12.18 \n Major Version Upgrade Support for PostgreSQL 16 (Preview) \n Real-time machine learning prediction with Azure_AI Extension (Preview) \n New Monitoring Metric Database Size (Preview) \n Azure Postgres Team at PG-Conf-India 2024 \n \n \n 1. Migration Service in Azure Database for PostgreSQL (General Availability) \n \n \n The migration service in Azure Database for PostgreSQL simplifies the process of moving your PostgreSQL instances to Azure, offering migration options from an AWS RDS for PostgreSQL, Azure Database for PostgreSQL - Single Server, on-premises servers, and Azure virtual machines (VMs). The migration service is designed to help you move PostgreSQL instances to Azure Database for PostgreSQL - Flexible Server with ease and confidence. Some advantages of using the migration service include: \n \n Managed migration service. \n Support for schema and data migrations. \n No complex setup. \n Simple to use portal/cli based migration experience. \n No limitations in terms of the size of databases it can handle. \n \n For a deep dive into the capabilities and step-by-step guidance on using the Migration Service in Azure Database for PostgreSQL, please refer to our official documentation and blog. \n \n 2. Support for the latest Postgres minor versions 16.2, 15.6, 14.11, 13.14, and 12.18 \n \n PostgreSQL minor versions 16.2, 15.6, 14.11, 13.14, and 12.18 are now supported by Azure Database for PostgreSQL – Flexible Server. These minor version upgrades are automatically performed as part of the monthly planned maintenance in Azure Database for PostgreSQL – Flexible Server. This upgrade automation ensures that your databases are always running on the most secure and optimized versions without requiring manual intervention. This release fixes two security vulnerabilities and over 40 bug fixes and improvements. To learn more, please refer PostgreSQL community announcement for more details about the release. \n \n 3. Major Version Upgrade Support for PostgreSQL 16 (Preview) \n \n \n Azure Database for PostgreSQL Flexible Server now supports in-place major version upgrades to PostgreSQL 16 in preview. This update offers access to the latest PostgreSQL features with minimal downtime and a simplified upgrade process. \n Key Benefits: \n \n New Features: Leverage enhancements like pg_stat_io for I/O monitoring, improved query execution, and advanced replication. \n Ease of Upgrade: Upgrade directly without data migration or changing server settings. \n Minimal Downtime: Our streamlined process ensures a smooth transition with reduced production disruptions. \n \n For a detailed guide on upgrading, visit our documentation: Major Version Upgrade \n \n 4. Real-time machine learning predictions with Azure_AI Extension (Preview) \n \n \n Now in preview, applications can invoke machine learning models hosted on Azure Machine Learning online endpoints to enable real-time predictions right within the database. The Azure_AI extension simplifies this integration providing the ability with a UDF call to invoke pre-trained or custom and fine-tuned models hosted on Azure machine Learning. \n \n For more details please refer - Real-time predictions with the azure_ai extension (Preview) - Microsoft Community Hub \n \n 5. New Database Size monitoring metric (Preview) \n \n \n The new \"Database Size\" metric in Azure Database for PostgreSQL Flexible Server significantly enhances database management by providing precise, granular insights into the storage consumption of individual databases. This feature aids in detailed monitoring, supports effective capacity planning by offering exact data on database sizes, and promotes operational efficiency through actionable insights, crucial for optimizing database performance and resource utilization in a multi-database environment. For a complete list of monitoring metrics available for Azure PostgreSQL Flexible Server, please refer our documentation. \n \n 6. Azure Postgres Team at PG-Conf-India 2024 \n \n \n Microsoft demonstrated its commitment to the PostgreSQL community through its Platinum sponsorship of PGConf India 2024, held in Bengaluru. The event, attended by over 400+ participants, saw significant involvement from the Microsoft Postgres team. The team engaged in numerous activities, including delivering talks, conducting trainings and managing a well-received booth. \n \n Sessions Delivered \n \n Keynote: All the Postgres Things at Microsoft – By charlesfeddersenMS \n Postgres load balancing is secretly broken: The cancellation problem – By JelteF \n Transforming PostgreSQL to API: A Simplified Journey with Data API Builder – By varun-dhawan \n pg_upgrade like a boss! – By Alexander Kukushkin \n Training: Building AI Applications with Azure OpenAI and PostgreSQL – By AlicjaKucharczyk & adityaduvuri \n \n \n In Conclusion \n As March 2024 comes to a close, we're excited about the new updates we've brought to Azure Database for PostgreSQL Flexible Server. This month, we have made it easier for you to move and manage your databases with the launch of our Migration Service options. We also introduced latest minor versions, a sneak peek at upgrades with PostgreSQL 16, smarter ways to use AI with your database, and a new tool to keep an eye on your database size. These changes are all about making things better and simpler for you. Furthermore, our participation in PG-Conf-India 2024 as a Platinum sponsor showcased our deep commitment to the PostgreSQL community. Through talks, training, and sessions delivered by our experts, we engaged with over 400 participants, sharing insights and exploring the latest in PostgreSQL technology. This involvement underscores our dedication not just to innovation within our services but also to fostering a vibrant, informed community around Azure PostgreSQL. \n Looking back at March, we're proud of what we've added to our service and grateful for the community's support and feedback. We can't wait to see how you use these new features in your work. Please keep sharing your thoughts with us; they really help us improve. Stay tuned for more updates, and thanks for being with us on this journey! ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"7121","kudosSumWeight":2,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MTA3Mjc1LUExUlgwcw?revision=49\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MTA3Mjc1LTU2OTM3MWkzRjJDOUM0QTBGNzQxNzQ3?revision=49\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MTA3Mjc1LTU2OTM2NWk2NUNEREYzQkIxM0EyMDNE?revision=49\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDQ","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MTA3Mjc1LTU2OTM2NmlBRkNCOEVDQTU0QjBFMDlB?revision=49\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDU","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MTA3Mjc1LTU2OTM2N2lBMTMxRUJGMzdGNDlBMDI5?revision=49\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDY","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MTA3Mjc1LTU2OTM2OGlFQ0ZDREE4MTRGMTYxM0VE?revision=49\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDc","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MTA3Mjc1LTU2OTM2OWk2MTcxNUVGQzU1NUY4MDRC?revision=49\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDg","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MTA3Mjc1LTU2OTM3MGlBQTdGN0U4QTYyREQ2NjhD?revision=49\"}"}}],"totalCount":8,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":{"__typename":"UploadedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MTA3Mjc1LUExUlgwcw?revision=49"},"coverImageProperties":{"__typename":"CoverImageProperties","style":"WIDE","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4401044":{"__typename":"Conversation","id":"conversation:4401044","topic":{"__typename":"BlogTopicMessage","uid":4401044},"lastPostingActivityTime":"2025-04-04T09:55:35.184-07:00","solved":false},"User:user:2019392":{"__typename":"User","uid":2019392,"login":"RashaudSavage","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS0yMDE5MzkyLUNhZ2kyaA?image-coordinates=0%2C445%2C5488%2C5933"},"id":"user:2019392"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxMDQ0LXVndVFabA?revision=1\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxMDQ0LXVndVFabA?revision=1","title":"Azure_Microsoft_Mistral 4.jpg","associationType":"COVER","width":1600,"height":900,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxMDQ0LVBYMDNnaA?revision=1\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxMDQ0LVBYMDNnaA?revision=1","title":"Mistral-FT-gif.gif","associationType":"BODY","width":3203,"height":1773,"altText":""},"BlogTopicMessage:message:4401044":{"__typename":"BlogTopicMessage","subject":"Serverless Fine Tuning Now In More US-Regions!","conversation":{"__ref":"Conversation:conversation:4401044"},"id":"message:4401044","revisionNum":1,"uid":4401044,"depth":0,"board":{"__ref":"Blog:board:MachineLearningBlog"},"author":{"__ref":"User:user:2019392"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":214},"postTime":"2025-04-04T09:55:35.184-07:00","lastPublishTime":"2025-04-04T09:55:35.184-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" We value your feedback and recognize the demand for fine tuning to be accessible in more regions. Today, we are excited to announce that serverless finetuning for Mistral, Phi, and NTT models is now available across all US regions where base model inferencing is also accessible. This expansion aims to provide greater flexibility and accessibility for users, ensuring that everyone can benefit from the enhanced capabilities of serverless finetuning. \n \n Region Availability \n Cross region finetuning is now enabled in the following regions: \n \n EastUS \n EastUS2 \n SouthCentralUS \n NorthCentralUS \n WestUS \n WestUS3 \n \n Model Availability \n \n Mistral-Nemo \n Mistral-Large-2411 \n Ministral-3B \n Phi-3.5-mini-instruct \n Phi-3.5-MoE-instruct \n Phi-4-mini-instruct \n Tsuzumi-7b \n \n \n \n \n Looking Ahead: More Models and Regions \n As we continue to innovate and expand our model offerings, more models and regions will soon be supported. Our team is working diligently to ensure that users across various locations can benefit from the latest advancements in serverless finetuning. Stay tuned for updates as we roll out these enhancements, providing even greater flexibility and accessibility for our global user base. We appreciate your ongoing support and look forward to sharing more details in the near future. \n \n Get started today! \n Whether you're a newcomer to fine-tuning or an experienced developer, getting started with Azure AI Foundry is now more accessible than ever. Fine-tuning is available through both Azure AI Foundry and Azure ML Studio, offering a user-friendly interface for those who prefer a graphical user interface (GUI) and SDK’s and CLI for advanced users. \n Learn more! \n \n Try it out with Azure AI Foundry \n Explore documentation for the model catalog in Azure AI Foundry \n Begin using the Finetuning SDK in the notebook \n Learn more about Azure AI Content Safety - Azure AI Content Safety – AI Content Moderation | Microsoft Azure \n Get started with Finetuning on Azure AI Foundry \n Learn more about region availability \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"2214","kudosSumWeight":3,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxMDQ0LXVndVFabA?revision=1\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxMDQ0LVBYMDNnaA?revision=1\"}"}}],"totalCount":2,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":{"__typename":"UploadedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxMDQ0LXVndVFabA?revision=1"},"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4398388":{"__typename":"Conversation","id":"conversation:4398388","topic":{"__typename":"BlogTopicMessage","uid":4398388},"lastPostingActivityTime":"2025-04-01T01:52:37.580-07:00","solved":false},"Blog:board:AnalyticsonAzure":{"__typename":"Blog","id":"board:AnalyticsonAzure","displayId":"AnalyticsonAzure","nodeType":"board","conversationStyle":"BLOG","title":"Analytics on Azure Blog","shortTitle":"Analytics on Azure Blog","parent":{"__ref":"Category:category:Azure"}},"User:user:201631":{"__typename":"User","uid":201631,"login":"LindseyAllen","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS0yMDE2MzEtNTgyNzc3aTc0MEE1QkQxRDZFMjYyMDc"},"id":"user:201631"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk4Mzg4LU9kNGp1RQ?revision=3\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk4Mzg4LU9kNGp1RQ?revision=3","title":"image.png","associationType":"BODY","width":559,"height":125,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk4Mzg4LXFIeXpDTg?revision=3\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk4Mzg4LXFIeXpDTg?revision=3","title":"image (6).png","associationType":"BODY","width":1920,"height":1080,"altText":""},"BlogTopicMessage:message:4398388":{"__typename":"BlogTopicMessage","subject":"Power BI & Azure Databricks: Smarter Refreshes, Less Hassle","conversation":{"__ref":"Conversation:conversation:4398388"},"id":"message:4398388","revisionNum":3,"uid":4398388,"depth":0,"board":{"__ref":"Blog:board:AnalyticsonAzure"},"author":{"__ref":"User:user:201631"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"We are thrilled to introduce a new Power BI task type to Azure Databricks Workflows!","metrics":{"__typename":"MessageMetrics","views":1357},"postTime":"2025-03-31T05:02:33.313-07:00","lastPublishTime":"2025-03-31T13:59:40.483-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n We are excited to extend the deep integration between Azure Databricks and Microsoft Power BI with the Public Preview of the Power BI task type in Azure Databricks Workflows. This new capability allows users to update and refresh Power BI semantic models directly from their Azure Databricks workflows, ensuring real-time data updates for reports and dashboards. By leveraging orchestration and triggers within Azure Databricks Workflows, organizations can improve efficiency, reduce refresh costs, and enhance data accuracy for Power BI users. \n Power BI tasks seamlessly integrate with Unity Catalog in Azure Databricks, enabling automated updates to tables, views, materialized views, and streaming tables across multiple schemas and catalogs. With support for Import, DirectQuery, and Dual Storage modes, Power BI tasks provide flexibility in managing performance and security. This direct integration eliminates manual processes, ensuring Power BI models stay synchronized with underlying data without requiring context switching between platforms. \n Built into Azure Databricks Lakeflow, Power BI tasks benefit from enterprise-grade orchestration and monitoring, including task dependencies, scheduling, retries, and notifications. This streamlines workflows and improves governance by utilizing Microsoft Entra ID authentication and Unity Catalog suite of security and governance offerings. \n \n We invite you to explore the new Power BI tasks today and experience seamless data integration—get started by visiting the [ADB Power BI task documentation]. ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"1569","kudosSumWeight":0,"repliesCount":1,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk4Mzg4LU9kNGp1RQ?revision=3\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk4Mzg4LXFIeXpDTg?revision=3\"}"}}],"totalCount":2,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4386980":{"__typename":"Conversation","id":"conversation:4386980","topic":{"__typename":"BlogTopicMessage","uid":4386980},"lastPostingActivityTime":"2025-03-31T21:52:51.335-07:00","solved":false},"User:user:2893153":{"__typename":"User","uid":2893153,"login":"manniarora","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS0yODkzMTUzLWRYeVF6YQ?image-coordinates=0%2C20%2C365%2C385"},"id":"user:2893153"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzg2OTgwLVY2eEx3Tw?revision=2\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzg2OTgwLVY2eEx3Tw?revision=2","title":"The Future of AI.png","associationType":"COVER","width":641,"height":644,"altText":""},"BlogTopicMessage:message:4386980":{"__typename":"BlogTopicMessage","subject":"The Future of AI: Harnessing AI for E-commerce - personalized shopping agents","conversation":{"__ref":"Conversation:conversation:4386980"},"id":"message:4386980","revisionNum":2,"uid":4386980,"depth":0,"board":{"__ref":"Blog:board:AIPlatformBlog"},"author":{"__ref":"User:user:2893153"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" Explore the development of personalized shopping agents that enhance user experience by providing tailored product recommendations based on uploaded images. Leveraging Azure AI Foundry, these agents analyze images for apparel recognition and generate intelligent product recommendations, creating a seamless and intuitive shopping experience for retail customers. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":709},"postTime":"2025-02-28T07:15:00.018-08:00","lastPublishTime":"2025-02-28T07:15:00.018-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" The Future of AI blog series is an evolving collection of posts from the AI Futures team in collaboration with subject matter experts across Microsoft. In this series, we explore tools and technologies that will drive the next generation of AI. Explore more at: https://aka.ms/the-future-of-ai \n Harnessing AI for eCommerce - personalized shopping agents \n In the dynamic sector of eCommerce, personalization is crucial for enhancing user experience and driving engagement. Retailers are leveraging AI to create personalized shopping agents with multimodal capabilities. \n Imagine being able to upload an image of a celebrity’s outfit and instantly receiving product recommendations that match the style. This seamless interaction is made possible by orchestrating specialized AI skills and agents. Let’s dive into how we built our personalized shopping agent to bring this vision to life. \n How Our AI Shopping Agent Works \n Our system is built using multiple services from Azure AI Foundry. At the center it has an orchestrator or a state machine and a couple of independent skills/agents with carefully crafted prompts, to deliver accurate and personalized recommendations. Watch our eCommerce agent demo. \n \n Step 1: Query-preprocess: Content Moderation & Rephrasing \n When a user uploads an image, the system first processes it through the Azure AI Content Safety service to ensure compliance and prevent inappropriate or harmful content from entering the system. Additionally, the query is rephrased to incorporate all relevant context from the conversation history, ensuring a more accurate and meaningful search experience. \n Step 2: Image Analysis & Apparel Recognition \n If the image passes moderation, the orchestrator invokes an image describer agent, which identifies key apparel elements. For example, if a celebrity is wearing a blue suit, the system detects this and provides structured metadata. The agent may then suggest complementary pieces, such as a matching button-down shirt. \n Step 3: Smart Product Recommendations \n With the context preserved, the recommender skill is triggered. It generates search queries tailored for searching the catalog index—queries that are more targeted to produce highly relevant and personalized results. Using these search queries, the search skill is triggered by the orchestrator to find relevant products. \n The Technology Behind Our AI Shopping Agent \n Data Ingestion and Indexing \n Our ingestion service processes large volumes of product catalog data, which are then vectorized and indexed using Azure AI Search. This enables semantic search capabilities, allowing users to find products not just through keywords but through meaning-based searches. \n Enhancing Product Descriptions with AI \n We leverage Azure OpenAI Service to enrich product descriptions, standardize categories, and improve overall searchability. By doing so, we enhance product discoverability and ensure customers receive highly relevant results. \n Session Management for Seamless Interactions \n The session manager preserves user context across interactions, enabling the aggregation of intermediate results and delivering real-time responses to enhance user engagement throughout the shopping journey. To ensure efficient communication, a message queue between the orchestrator and the session manager facilitates seamless sharing of intermediate results. \n AI Orchestration and Tailored Responses \n The AI orchestrator and domain-specific skills use the Azure OpenAI Service chat completion API to generate conversational, context-aware responses. This creates a smooth and intuitive shopping experience. \n Evaluating for High-Quality AI Performance \n Evaluation plays a critical role in the AI development lifecycle. Using Azure AI Foundry evaluation tools, we rigorously tested our solution to ensure it meets the highest standards for accuracy, relevance, and responsiveness. \n We're excited about the potential of personalized shopping concierge agents and look forward to seeing how they enhance e-commerce experience. \n To customize your retail agent: \n \n Check out the retail solution accelerator developed by the Azure CoreAI Applied Engineering Team \n Explore Azure AI Foundry \n Start using the Azure AI Foundry SDK \n Review the Azure AI Foundry documentation \n Take the Azure AI Learn courses \n Learn more about Microsoft Cloud for Retail \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"4552","kudosSumWeight":5,"repliesCount":3,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzg2OTgwLVY2eEx3Tw?revision=2\"}"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[{"__typename":"VideoEdge","cursor":"MHxodHRwczovL3lvdXR1LmJlL3F1Y1JwaEx1N1o4LzE3NDA2OTM3NDc1Njd8MHwyNTsyNXx8","node":{"__typename":"AssociatedVideo","videoTag":{"__typename":"VideoTag","vid":"https://youtu.be/qucRphLu7Z8/1740693747567","thumbnail":"https://i.ytimg.com/vi/qucRphLu7Z8/hqdefault.jpg","uploading":false,"height":240,"width":320,"title":null},"videoAssociationType":"INLINE_BODY"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":{"__typename":"UploadedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzg2OTgwLVY2eEx3Tw?revision=2"},"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4397140":{"__typename":"Conversation","id":"conversation:4397140","topic":{"__typename":"BlogTopicMessage","uid":4397140},"lastPostingActivityTime":"2025-03-26T19:27:17.907-07:00","solved":false},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk3MTQwLXpFeXl4aA?revision=8\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk3MTQwLXpFeXl4aA?revision=8","title":"image.png","associationType":"BODY","width":358,"height":80,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk3MTQwLUR2WTVIRQ?revision=8\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk3MTQwLUR2WTVIRQ?revision=8","title":"1742998745484.gif","associationType":"BODY","width":1440,"height":1440,"altText":""},"BlogTopicMessage:message:4397140":{"__typename":"BlogTopicMessage","subject":"Anthropic State-of-the-Art Models Available to Azure Databricks Customers","conversation":{"__ref":"Conversation:conversation:4397140"},"id":"message:4397140","revisionNum":8,"uid":4397140,"depth":0,"board":{"__ref":"Blog:board:AnalyticsonAzure"},"author":{"__ref":"User:user:201631"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"We are thrilled to introduce even more model choices with the arrival of Anthropic Claude 3.7 Sonnet in Azure Databricks!\n","metrics":{"__typename":"MessageMetrics","views":3498},"postTime":"2025-03-26T07:11:14.886-07:00","lastPublishTime":"2025-03-26T19:27:17.907-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n Our customers now have greater model choices with the arrival of Anthropic Claude 3.7 Sonnet in Azure Databricks. Databricks is announcing a partnership with Anthropic to integrate their state-of-the-art models into Databricks Data Intelligence Platform as a native offering, starting with Claude 3.7 Sonnet http://databricks.com/blog/anthropic-claude-37-sonnet-now-natively-available-databricks. With this announcement, Azure customers can use Claude Models directly in Azure Databricks. Foundation model REST API reference - Azure Databricks | Microsoft Learn \n With Anthropic models available in Azure Databricks, customers can use the Claude \"think\" tool with business data optimized promote to guide Claude efficiently perform complex tasks. With Claude models in Azure Databricks, enterprises can deliver domain-specific, high quality AI agents more efficiently. As an integrated component of the Azure Databricks Data Intelligence Platform, Anthropic Claude models benefit from comprehensive end-to-end governance and monitoring throughout the entire data and AI lifecycle with Unity Catalog. \n With Claude models, we remain committed to providing customers with model flexibility. Through the Azure Databricks Data Intelligence Platform, customers can securely connect to any model provider and select the most suitable model for their needs. They can further enhance these models with enterprise data to develop domain-specific, high-quality AI agents, supported by built-in custom evaluation governance across both data and models. \n \n 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