Browse Machine Learning Community (21)

Wiliam_Rosa breaks down how Spark Structured Streaming’s Real-time mode (as implemented in Azure Databricks) can cut anomaly-detection latency from seconds to sub-millisecond, using an Ethereum transaction pipeline that classifies events as ALLOW or QUARANTINE based on protocol invariants and sensitive-payload checks.

KimVaddi explains why many oil & gas AI initiatives stall on inconsistent business meaning rather than data volume, and argues that the “meaning layer” is the real readiness bottleneck. The post connects OSDU, Azure Data Manager for Energy, and Microsoft Fabric IQ’s preview ontology capability as a practical path to governed, cross-domain reasoning.

bharadwajr explains how the Azure team built GEM (Great Experiences Matter), an AI-enabled “feedback-to-fix” loop that aggregates customer feedback at scale, turns it into prioritized insights, and creates actionable Azure DevOps work items with an auditable evidence trail and human review.

newmancheng explains how Microsoft Discovery combined agentic AI, computational predictors, and wet-lab feedback to design and validate a novel organic negolyte for redox flow batteries, with a focus on preserving negative results as durable knowledge that improves subsequent design rounds.

pranabpaul walks through the first part of building a retail assistant demo using Microsoft Fabric and Azure AI Foundry, focusing on setting up the Azure environment, provisioning Fabric capacity, loading sample data into a Lakehouse, creating an ontology, and wiring a Fabric Data Agent to query that data.

wolfgangdesalvador breaks down Azure’s MLPerf Storage v3.0 results for Azure Managed Lustre, focusing on how sustained throughput and fast checkpoint I/O help keep large AI training clusters from stalling.

Rafia Aqil explains how to diagnose and resolve Azure Databricks “capacity” incidents by separating true regional VM SKU shortages from subscription quota limits and Databricks platform ceilings, then applying practical mitigations like SKU swaps, off-peak retries, instance pools, and capacity reservation groups.

Rafia Aqil (co-authored with Sanjeev Nair) lays out a three-phase, practical approach to reducing Azure Databricks spend: first getting visibility into where DBUs and cloud costs go, then tightening cluster and code practices, and finally setting up ongoing cost observability with Unity Catalog system tables, tagging, dashboards, and budgets.

Vishnu Charan TJ explains how new integrations between Azure Blob Storage and the NVIDIA Dynamo stack can speed up LLM inference on AKS, focusing on faster model weight loading for cold starts and KV cache offloading to reduce time-to-first-token latency.

Paul_VicenteH explains why retrieval and reranking are separate stages in a RAG pipeline, then demonstrates hybrid retrieval (including RRF) and learned reranking across Azure AI Search, Azure SQL, PostgreSQL Flexible Server, and Azure Cosmos DB using the SQuAD dataset.

Pamela Fox shares the materials from the “Microsoft IQ Deep Dive with Python” livestream series, showing how to ground AI apps and agents using Web IQ, Work IQ, Fabric IQ, and Foundry IQ via MCP endpoints, plus code samples, slides, and write-ups for building and deploying Python agents.

nelsontam introduces Microsoft’s MARS (Map Autoregressive) model on Microsoft Foundry and explains how it converts satellite imagery into GIS-ready vector map data. The post outlines an end-to-end workflow with Microsoft Planetary Computer Pro, including STAC/COG ingestion requirements and practical deployment settings for running geospatial feature extraction at scale.

Ilana Waitser announces a public preview that lets you share Azure Monitor Logs from a Log Analytics workspace into Microsoft Fabric’s OneLake as Delta Parquet, without duplicating data. The post explains what this enables in Fabric—Power BI reporting, Spark-based analytics and ML, and correlating telemetry with business data for near real-time decisions.

Rafia Aqil outlines how to enable Azure Databricks’ Compliance Security Profile (CSP) for HIPAA workloads, including the September 1, 2026 deadline, required prerequisites like Azure VNet encryption and supported VM series, and a rollout approach to validate cluster startup and end-to-end connectivity before production.

Nora Zhan introduces “Physical-World Intelligence” and explains how GeoAI combines geospatial data (weather, satellite imagery, sensors, and maps) with enterprise context and AI. The article outlines Microsoft Foundry’s geospatial model catalog, Planetary Computer Pro as a GeoAI data plane, and a GeoAI SDK for production-scale inference workflows.

Naba Kumar and the MLVC team announce the open-source release of MLVC, a learned video codec that replaces traditional codec primitives with end-to-end neural compression. The post shares bitrate savings versus H.264/H.265, real-time performance targets on commodity NPUs, and what’s included in the GitHub release (models, weights, training scripts, and conversion tooling).

Rafia Aqil explains Microsoft’s IQ Platform (Work IQ, Fabric IQ, and Foundry IQ) and how it adds business and organizational context to AI systems. The post breaks down Fabric’s OneLake-based data layer, ontology-driven meaning, and Foundry IQ’s managed services for RAG, memory, ranking, and citations.

RajyaLaxmiYellajosyula announces the Oracle AI Database@Azure AI adoption playbook and outlines the main blueprint patterns for building AI experiences on Oracle data using Microsoft services, with a strong emphasis on security, governance, and regulated-industry requirements.

Pamela Fox announces a free 3-part livestream series that teaches developers how to use Microsoft IQ (Foundry IQ, Work IQ, and Fabric IQ) from Python to ground AI agents in organizational knowledge, workplace context, and structured data, with runnable code shared in an open-source repo.

Shantanu Patankar and Azin Heidarshenas break down Azure’s MLPerf Training v6.0 run for Llama 3.1 405B, sharing what they learned scaling pretraining to 8,192 NVIDIA GB200 GPUs on Fairwater—where the time goes per step, why topology-aware parallelism mapping matters, and what actually limits scaling efficiency at extreme scale.

Phil Vetter and Lee Jones describe how Exclaimer evolved its global email-signature platform on Microsoft Azure, moving from VM-heavy deployments to AKS-based microservices and adopting purpose-fit data stores (Azure SQL, PostgreSQL, Cosmos DB, Data Explorer, Databricks) to improve scaling, reliability, and cost.