Browse All Machine Learning Content (299)

John Savill rounds up a week of Azure platform changes and retirements, spanning compute/storage updates, database and identity improvements, monitoring changes, and several developer-facing AI items including GitHub Copilot Agent Mode in SSMS and Azure AI Foundry agent licensing and model availability.

BhaktiRath95 walks through common failure modes when running AI/ML inference workloads on Azure Container Apps, including slow model startup, probe timeouts, OOM kills, and GPU initialization problems. The post provides concrete probe settings, Python/FastAPI patterns, and Log Analytics queries to diagnose and fix issues methodically.

MichalBar introduces a preview redesign of Microsoft Fabric Real-Time Dashboards tile editing, adding Copilot-assisted visual authoring alongside a more code-friendly KQL workflow. The post walks through creating a visual from a prompt or query, iterating with history, and testing parameterized queries directly in the editor.

MichalBar introduces the Time Series Visualization (Preview) for Microsoft Fabric Real-Time Dashboards, focusing on how to explore multivariate time series data with series search and grouping, synchronized time-range navigation, and visual customization options like adaptive scaling and linear/log axis modes.

Laurent Bugnion explains why Microsoft Build 2026 labs are a popular way for developers to learn through hands-on sessions, including tracks that cover AI, Copilot, and Microsoft Fabric. He also shares where to find the on-demand “Digital lab” sessions and how long they remain available after the event.

Dan Wahlin demonstrates an “agentic journey” workflow that takes an app idea through planning, coding, infrastructure creation, and deployment to Azure, using GitHub Copilot CLI and Azure skills to handle tasks like Bicep templates, health probes, and database wiring for an app backed by Azure SQL and Microsoft Foundry.

MichalBar announces general availability of Live Refresh for Real-Time Dashboards in Microsoft Fabric, an event-driven refresh model that updates visuals when new data is ingested. The post explains how it reduces polling overhead, supports pausing/resuming during investigation, and provides configuration options like fallback refresh intervals.

analyticanna introduces Rayfin (Preview), an open-source SDK and CLI for turning content like markdown into shareable, hosted sites deployed as first-class items in Microsoft Fabric, with stable URLs, workspace-based access control, and data captured into a SQL database in Fabric for use across OneLake workloads.

Rafia Aqil explains how to diagnose and respond when Azure Databricks clusters can’t start or scale due to Azure regional VM capacity constraints, including what to send to Microsoft support, which VM families to switch to, and longer-term design choices like instance pools, serverless compute, and multi-region deployments.

SindhuBharadwaj introduces a Fabric-first migration flow that lets you mount an Azure Data Factory instance inside a Fabric workspace and migrate selected pipelines without switching portals. The post outlines the migration steps, supported connection/authentication mappings, and the validation work to do before re-enabling triggers in production.

Aaron Merrill announces a preview feature in Microsoft Fabric’s OneLake catalog that lets admins assign, edit, and remove workspace role memberships across multiple workspaces from the Secure tab, helping teams standardize access controls and reduce drift as Fabric estates scale.

Rafia_Aqil outlines a reference architecture for ingesting both streaming and batch data through Microsoft Fabric into Azure Databricks, using OneLake/ADLS and a medallion (Bronze/Silver/Gold) layout. The post breaks down five Fabric-to-Databricks integration paths and calls out security, governance, and monitoring considerations.

Sharlkaur introduces a preview workflow for AI-authored Power BI reports in Microsoft Fabric, using Skills for Fabric and the Power BI authoring plugin optimized for GitHub Copilot CLI. The post shows how agents can generate PBIR-based reports from prompts, iterate using screenshots, and publish to Fabric as part of an end-to-end agentic analytics flow.

shiv_narayanan announces general availability of SharePoint and OneDrive Shortcuts in Microsoft Fabric OneLake, explaining how Fabric can reference Microsoft 365 files in place, optionally transform supported formats into Delta tables, and use Entra ID-based identities for production authentication and scale.

Sally Dabbah explains how to turn Synapse/ADF/Microsoft Fabric pipeline failures into structured, queryable telemetry by sending standardized failure events into Azure Monitor Log Analytics via the Logs Ingestion API and a Data Collection Rule, enabling KQL-based analysis, alerting, and reliability reporting across environments and datasets.

This week in ML, Microsoft Fabric moved closer to an agent-ready analytics platform, with new ways to ship backends into Fabric, ground agents in governed context, and model relationships directly on OneLake. Rayfin positions Fabric as a default deployment target for data-powered apps, while Fabric IQ (now GA) and its ontology support aim to standardize how agents request context with permissions and auditability built in. Graph in Fabric (GA) adds GQL-based relationship querying, and the Fabric Operations agent plus Fabric Skills show how Microsoft wants teams to monitor, automate, and code against Fabric with guardrails instead of one-off scripts.

John Savill runs through a Build-special weekly Azure update, covering a wide set of platform announcements across compute, containers, integration, monitoring, databases, Fabric/Databricks, and Azure AI Foundry—plus security-focused items like confidential computing and Purview agent integrations.

Charles Feddersen and Abe Omorogbe explain how AI apps and agents change database design, focusing on reasoning over operational data instead of only transactions. They demo new capabilities across Azure SQL Database, Azure Cosmos DB, and Azure HorizonDB (cloud-native PostgreSQL) to simplify architectures and reduce latency.

Milos Colic shares how Xoople scaled Python-based AI workloads on Azure using Ray via Anyscale, covering the distributed-systems challenges behind data ingestion, training, and inference, and why the team prioritized delivering outcomes over operating clusters.

Nikisha Reyes-Grange introduces Azure HorizonDB and Rayfin, focusing on how these Azure Data and Microsoft Fabric innovations aim to modernize PostgreSQL operations and simplify building and running data applications, including SQL-level AI functions and hybrid search concepts.

Marco Casalaina and Ayca Bas explain how to build grounded, enterprise-ready agents by delegating context to three “IQ” systems: Foundry IQ for knowledge, Fabric IQ for data, and Work IQ for human/work context, with a live demo showing how this approach avoids brittle, hand-wired pipelines.

tinotereshko rounds up the Build 2026 announcements for Microsoft Fabric Data Warehouse, covering GPU-based query acceleration, configurable data retention and time travel, SQL editor upgrades (including inline Copilot chat), and new T-SQL capabilities. The post also highlights DevOps-focused features like REST APIs and Git/DacFx-based schema deployments for the SQL analytics endpoint.

miguel shares benchmark results for Microsoft Fabric Dataflow Gen2 (CI/CD), explaining which performance lever to use (Fast Copy, Modern Evaluator, partitioned compute, staging) depending on whether your bottleneck is ingestion throughput, transformation-heavy Power Query (M), or parallel file processing.

prl announces enhancements to the Migration Assistant for Microsoft Fabric Data Warehouse, focusing on making enterprise data warehouse migrations less manual and easier to execute with a more guided workflow.

miguel announces Microsoft Build updates for Microsoft Fabric Dataflow Gen2 and Power Query, focusing on scaling low-code data transformation with Spark-backed execution, improving reuse of query logic across dataflows, and modernizing the Power Query “Get Data” experience in Power BI Desktop.

WillT announces the general availability of Microsoft Fabric Operations agent, describing how it uses LLM-driven rule generation plus Real-Time Intelligence monitoring to detect issues, ask clarifying questions, and (with approval) run remediation actions like pipelines, notebooks, UDFs, and Power Automate workflows, with tracing and governance built in.

Microsoft Developer’s Data Exposed episode shows how to build a data-powered application using Rayfin with a Microsoft Fabric SQL Database backend, including Fabric SSO authentication. It also covers iterating on the app with GitHub Copilot and how Rayfin’s code-first SDK reduces the amount of infrastructure wiring you need to do.

makromer recaps the Build 2026 updates for Microsoft Fabric Data Factory orchestration, focusing on new pipeline capabilities like human-in-the-loop approvals, conditional retries, refresh activities for Materialized Lake View and SQL endpoints, a modernized pipeline canvas, expanded Variable Libraries (including Airflow), and in-workspace migration from Azure Data Factory.

Amanda Silver and Marco Casalaina explain how to build context-aware AI agents by connecting them to enterprise knowledge, business data, and work signals using Foundry IQ, Fabric IQ, and Work IQ, with an emphasis on orchestration, governance, and operating within trusted boundaries.

Mark Russinovich and Ion Stoica discuss how AI platforms need to evolve for agentic, multimodal, globally distributed workloads, covering infrastructure fundamentals, training and real-time serving architectures, and why open source, security, and governance are becoming core platform requirements.

Sunitha Muthukrishna demonstrates how to use Rayfin with Microsoft Fabric to generate and deploy an agent-driven full-stack web app, including a managed database, authentication, and backend services, then connect it to an existing Fabric data estate to add analytics, BI, and AI-powered experiences.

Alexander Wachtel shows how Microsoft Fabric can be used to build dynamic, context-aware multi-agent workflows, using Fabric foundations like semantic models, lakehouses, and pipelines to support planning, retrieval, summarization, and execution across specialized agents.

Rishabh Saha shares how Microsoft and PepsiCo engineers modernized PepsiCo’s data foundation for agentic applications, using Azure SQL, Cosmos DB, PostgreSQL, and Azure Databricks. The session outlines a practical build path for agentic RAG, including Azure SQL vector indexing and semantic search to speed up repeatable app patterns.

Rob Ferguson leads a Microsoft Build 2026 panel on shipping custom AI models at scale, covering practical trade-offs in fine-tuning and serving, plus what teams are doing to control inference cost and latency in production.

Ben Zulauf and Sachin Patney present a Microsoft Build 2026 session on Rayfin’s approach to building backends as code, with agent-driven app generation and native integration with Microsoft Fabric data and analytics for deploying secure, scalable applications.

Parthasarathy Srinivasan and Rajya Laxmi Yellajosyula demonstrate a multi-cloud, enterprise AI workflow that combines Oracle Database@Azure with Microsoft Fabric, MCP, and GitHub Copilot, covering provisioning, synthetic data creation, ETL from bronze to gold, and an end-to-end fraud detection demo driven by natural-language orchestration.

Pablo Castro presents a Microsoft Build 2026 deep dive into Foundry IQ, Microsoft’s context engineering platform for building agents that can retrieve enterprise knowledge using agentic RAG. The session covers Foundry IQ’s architecture, connecting new knowledge sources, ingestion pipeline customization, retrieval APIs, and performance/evaluation improvements.

Vaidyaraman Sambasivam, Osi Otugo, and Jean Boudier demonstrate an end-to-end flow for taking Hugging Face open-source models from discovery to production inference using Foundry Managed Compute in Azure AI Foundry, focusing on scaling, governance, and avoiding direct GPU management.

William Liang demonstrates how teams use Azure AI Foundry to distill large models into smaller, task-focused language models using supervised fine-tuning, with an emphasis on reducing production latency and cost while maintaining accuracy through structured evaluation.

Katarina Stanley and Daniel Arrizza explain how Anyscale on Azure uses Ray on Azure Kubernetes Service (AKS) to run distributed AI workloads, from multimodal data pipelines and training/fine-tuning through to deploying models as inference services inside an Azure subscription.