Browse All Machine Learning Content (207)

Alper explains how Microsoft Fabric’s preview React visualization libraries support analytics apps built with AI coding agents. Using Vega-Lite specifications and shared data, theming, and interaction contracts, the components provide reusable charts and grids with formatting, scrolling, tooltips, and consistent styling rather than requiring each app to recreate these behaviors.

sbaynes describes using the Microsoft Discovery app to adapt insect-monitoring systems for the Asian citrus psyllid, a pest that spreads citrus greening disease. The case study covers grounded AI agents, experimental design, permitting, and synthetic biometric data for preliminary edge classifiers, with scientists reviewing decisions throughout the workflow.

Mariyaali introduces preview support for Fabric Data Warehouse in workspace monitoring. Completed distributed queries flow into a workspace-scoped KQL database, where administrators can compare performance, investigate failures, and build dashboards. Shared query identifiers connect workspace telemetry to Query Insights for deeper investigation within the affected warehouse.

Welcome to this week's ML roundup, covering confidential virtual machines on Azure Local, GPU-accelerated Fabric queries, and updates to Fabric's data and coding agents. We examine the upcoming query-generation rollout and November 6 Foundry integration migration deadline, then explore interactive visualizations, retrieval grounded in business data, and GPU memory profiling. Research reports highlight human-directed agents and explicit verification, while data-platform updates bring practical considerations for schemas, migrations, transactions, and governance, with availability and limitations distinguished throughout.

Ben Steenhoek and Ulugbek Abdullaev explain how configurable eagerness lets paid GitHub Copilot subscribers adjust inline suggestions in VS Code. They describe the low, medium, and high modes, the training approach behind them, reward-hacking mitigations, and experimental results showing how suggestion frequency and size change.

isaiahhoyer describes using Microsoft Discovery agents to adapt Microsoft Premonition biological monitoring systems for citrus pests. The case study covers grounding agents in engineering documents, designing field experiments, preparing permit requests, and generating synthetic wingbeat data for preliminary edge classifiers, with scientific review and human oversight throughout.

sbaynes introduces Flint-interactive, a language that helps AI agents generate interactive data visualizations (not just static charts) using high-level semantic events and update operators, so users can click, filter, zoom, and link interactions across charts, text, tables, and app state.

arindamc introduces Schema-aware Fabric Eventstreams (Preview), which lets Microsoft Fabric Real-Time Intelligence streams preserve event “shape” so teams can explore, classify, and process multiple event types in a single live stream without forcing everything into one wide schema.

MichalBar introduces a preview feature in Microsoft Fabric Real-Time Dashboards that uses Copilot to generate purpose-built custom visuals. By describing an operational question in natural language, dashboard authors can have Copilot create both the KQL query and an HTML-based visualization directly as a new dashboard tile.

Marko Hotti and Patty Chow demonstrate how to build a smarter AI travel planner on Azure Cosmos DB by combining operational app data with vector embeddings and search features, so the app can answer nuanced queries while still respecting real inventory and booking workflows.

madhurinrao explains ABFS Vectored Read for Azure Data Lake Storage and how it reduces request overhead for selective Parquet reads by coordinating noncontiguous byte ranges through the ABFS driver, enabling compatible Spark workloads to adopt the optimization via configuration.

Bruno Capuano and Luis Quintanilla explain how .NET’s AI building blocks fit together for real-world apps, focusing on data ingestion, embeddings, and vector search to ground LLM responses in your own documents and business data.

Wiliam_Rosa breaks down Azure Databricks multi-table, multi-statement transactions (GA as of Oct 16, 2026), explaining the two transaction modes, how conflict detection works, and why external clients need explicit retry logic to be reliable under real concurrency.

sbaynes reports on Microsoft Research work proving the KLS conjecture, a long-standing result in high-dimensional geometry with implications for sampling, optimization, and randomized algorithms. The post also describes a human-directed, agentic AI workflow used to explore candidate proof paths and support derivations and verification.

prl explains the preview incremental migration workflow in Migration Assistant for Microsoft Fabric Warehouse, focusing on migrating database object definitions in phases, re-running migrations without recreating the destination, and handling naming conflicts safely. The post also clarifies how object migration differs from data loading and where Data Factory copy jobs fit in.

prl introduces the preview of Fabric Migration Assistant for migrating Teradata workloads to Fabric Data Warehouse, focusing on translating metadata and SQL/BTEQ into Fabric-compatible T-SQL, reviewing exceptions, and choosing practical data-movement and validation approaches to reduce manual migration effort.

QixiaoWang introduces the Fabric Notebook Toolkit (FNTK) preview, a CLI that lets coding agents interact with live Microsoft Fabric notebooks—reading and editing cells, running code, and pulling outputs and Spark diagnostics—so iterative debugging and validation can happen directly from an agent-driven terminal workflow.

Ben Sickle outlines Microsoft’s announcements at NVIDIA GTC Berlin 2026 focused on running AI across Azure and customer-controlled environments, including Azure Local sovereign deployments, Foundry Local for on-prem model inference, confidential VMs, open-model endpoints for NVIDIA Nemotron via Fireworks AI on Azure, and GPU-accelerated analytics in Microsoft Fabric.

Johnson Shi explains how AKS GPU memory profiling helps you move from “GPU memory pressure” alerts to the exact functions responsible for allocations, using eBPF-based tracing and Grafana flame graphs so you can right-size GPU memory and validate fixes with repeatable tests.

Anna Hoffman explains how Microsoft SQL spans on-premises, Azure, and Microsoft Fabric, highlighting what’s new in the engine, developer tooling, and migration options. She also demonstrates AI-assisted performance troubleshooting and scalable vector search, and shows how Azure Arc/Azure Local and Foundry-based experiences can support modern data-driven apps.

Sukhwant Kaur demonstrates how SQL database in Microsoft Fabric can support app development, analytics, and AI in one place—covering database creation, workload migration, and automatic mirroring into OneLake, plus Git-based workflows with VS Code and a T-SQL RAG example for product search.

Patrick LeBlanc demonstrates Fabric Database Hub’s database agent for observing SQL Server databases, surfacing performance signals, and guiding investigation from detection through hands-on troubleshooting, including a blocking scenario in SSMS and follow-up analysis workflows in VS Code with GitHub Copilot support.

Pam Lahoud explains how to migrate SQL Server workloads to Azure Virtual Machines while keeping OS and database control, with practical guidance on VM sizing, storage selection, and cost considerations like constrained cores for licensing. She also outlines what to build next using Azure services and related Microsoft data/AI capabilities.

Bob Ward explains what “AI-ready” means in SQL Server 2025, focusing on native vector capabilities, embedding generation, and RAG patterns you can implement directly with T-SQL across local and cloud AI model deployments.

Alicia Li summarizes Q3 2026 updates to Microsoft Fabric Eventstreams, focusing on new managed and custom connectors, CDC ingestion from operational databases, schema registry and schema-aware processing for mixed event types, in-flight enrichment with Delta tables, improved monitoring and diagnostics, and stronger deployment and networking controls.

tushar-msft summarizes January–August 2026 Graph Query Language (GQL) updates in Microsoft Fabric Graph, covering new path-finding and traversal controls, path inspection functions, multi-stage query composition, richer aggregation options, and added value/text functions (casting, ranges, Unicode normalization, and regex helpers).

jovanpop-msft announces four preview T-SQL enhancements in Microsoft Fabric Data Warehouse—GROUP BY ALL, ORDER BY ALL, QUALIFY, and the FROM ... SELECT query format—aimed at reducing repetitive SQL, simplifying maintenance when projections change, and making analytical query logic easier to read.

jovanpop-msft announces a preview update for Microsoft Fabric Data Warehouse and the SQL analytics endpoint: new and enhanced T-SQL analytical functions for distribution analysis, including exact and approximate median/quantile calculations and expanded percentile aggregate syntax.

Microsoft Developer presents SQL Modernization Day, a set of sessions from the Microsoft SQL team focused on moving SQL Server workloads to Azure without rewriting applications, and then preparing those workloads for building AI-ready apps using modern Azure and Fabric SQL options.

jovanpop-msft introduces preview support for calling Fabric User Data Functions from T-SQL in Fabric Data Warehouse. The integration extends warehouse queries with custom Python logic for data enrichment, external API calls, and secret retrieval, with an example that calculates and stores IPv6 network prefixes for downstream analytics.

Amir Jafari announces the deprecation of the Fabric data agent “custom tool” integration in Microsoft Foundry and explains how to migrate to the Fabric IQ (OneLake Catalog) tool, including portal steps and an SDK/REST approach using MCP endpoints and Entra-based authentication.

midesa announces general availability of advanced NL2SQL and advanced DAX generation for Microsoft Fabric data agents, focused on improving query correctness for enterprise schemas. The post explains the agent’s explore-then-generate approach, new creator configurations (schema descriptions and topics), and internal benchmark results showing higher accuracy and lower latency.

This week's ML roundup focuses on the operational details that make data and AI systems reliable: consistent governance, predictable performance, and measurable evaluation. Azure Databricks reached GA with cross-engine ABAC for external Spark reads, while Microsoft Fabric added new ingestion paths, workload isolation controls, real-time event governance, and stronger admin recovery and network boundaries. On the infrastructure side, practical guides dug into GPU-enabled Kubernetes setups and efficient model serving on H100 Spot VMs with observability and KV-cache tuning. We also saw a push toward structured, testable model behavior with Olive and ONNX, plus real-world decision loops in F1 simulations and research on student simulators as eval harnesses and RL reward signals.

Wiliam_Rosa explains how Azure Databricks cross-engine ABAC enforces Unity Catalog row filters, column masks, and ABAC policies for external Apache Spark reads by routing queries through a Databricks-managed serverless enforcement layer, including required metastore settings, grants, Spark configuration, known limitations, and cost tracking.

sbaynes interviews Mercedes-AMG PETRONAS F1 lead race strategist Joey McMillan on how the team uses Microsoft Azure—especially Azure Batch—to run large-scale race simulations quickly enough to influence live, race-day strategy decisions under changing weather and track conditions.

RicardoDuncan rounds up the latest SQL developer tooling updates for VS Code, including new MSSQL extension features, GitHub Copilot schema-aware agent workflows, and Microsoft SQL “skills” for assessing and migrating SQL Server to Azure SQL, plus driver releases aligned with SQL Server 2025.

t-dmarcovich1 introduces Fabric Policies (preview), a governance feature for Microsoft Fabric that lets tenant and capacity admins control which actions are allowed for specific users and workspaces, with centralized management and auditing.

Microsoft Developer presents a Data Exposed session on Fabric Database Hub, focusing on bringing Azure SQL, SQL Server, Cosmos DB, PostgreSQL, and Fabric databases into a single place for discovery, estate-level visibility, governance, and AI-assisted insights to prioritize actions.

kinfey explains how to use small language models (SLMs) as typed “decision components” inside an application, then walks through a concrete pipeline using Microsoft Olive to LoRA fine-tune Qwen3.5-0.8B, export to CUDA FP16 ONNX, and validate decisions via option-letter probabilities.

sukkaur announces a preview feature that enables workspace-level Private Link connectivity for SQL database in Microsoft Fabric, aimed at tightening network access and reducing exposure by keeping database traffic on private networking paths.