Microsoft Fabric ML Model Endpoint remains "Activating" indefinitely after activation request
Hi, I'm using Microsoft Fabric ML Model Endpoints as part of an automated DEV → UAT promotion pipeline. Scenario The pipeline performs the following steps: Download model artifacts Validate SHA256 Register model versions in UAT Create/register UAT model versions successfully Activate the matching UAT model endpoint version Registration completes successfully. e.g result { "endpoint_http_status": 202, "endpoint_action_requested": true } Problem POST /activate → HTTP 202 Accepted `` the endpoint remins in Activating state forever Questions Is there a known issue where Fabric ML Model Endpoints can remain in Activating indefinitely after a successful activation request? Is there a recommended maximum time for an endpoint to remain in Activating before it should be considered failed? Is there a supported way to determine why endpoint provisioning is stuck? Should an endpoint that remains in Activating be: left alone, deactivated and reactivated, or recreated entirely? Are there diagnostic logs available for endpoint provisioning failures? Any guidance from the Fabric product team or community would be greatly appreciated. Thank you.
How does Fabric handle concurrent real-time ML inference requests?
## Regarding the scalability and performance of Fabric ML model endpoints. For our Use case, we're planning to deploy an XGBoost/LightGBM model in Fabric and consume the prediction endpoint through the application. Can the API support bulk predictions. For example, we may send 10 records in a single request, but we could also have multiple users calling the same endpoint simultaneously. I understand Fabric manages the underlying compute and supports automatic scaling, but I wanted to clarify a few things: How does Fabric handle concurrent requests to the same ML endpoint? Is the load balancing managed automatically? Are there any concurrency, throughput, or request-size limits we should consider? How does the scaling behave when the number of concurrent requests increases, and what impact does this have on response time and capacity consumption? Are there any recommended approaches for load testing or configuring the endpoint for production applications? Our main concern is ensuring that the prediction service doesn't affect the existing performance of the application. Has anyone tested Fabric ML endpoints with concurrent requests or used them for a similar application integration?How does the Fabric handle the real-time ML inference requests?
Regarding the scalability and the performance of Fabric ML endpoints. For the use case, we're planning to deploy the XGBoost/LightGBM model in Fabric and consume the prediction endpoint through the application. So, can the API support the bulk predictions. For Ex: we may send 10 records in a single request, but we could also have multiple users calling the same endpoint simultaneously. I understand that Fabric manages the underlying compute and supports the autoscaling, but wanted to clarify few things: How does Fabric handle concurrent requests to the same ML endpoints? Is the Load balancing managed automatically? Are there any concurrency, throughput, or request-size limits we should consider? How does the scaling behave when the number of concurrent request increases, and what impact does this have on the response time and capacity consumption? Are there any recommended approaches for load testing or configuring the endpoint for the production applications? Also our main concern is ensuring that the prediction service doesn't affect the existing performance of the application Has any one tested the Fabric ML endpoints with concurrent requests or used them for any similar application integration?
Smart Fleet Management
A real-time dashboard for better road safety, ETA optimization, and better routing to calibrate driving behavior parameters. Explore more at https://www.softwebsolutions.com/power-bi-consulting-services.html eyJrIjoiMTc2YTk3NWEtNzA1OS00ZmFkLTkyMjktNTZjNzM3ZGRjYjJjIiwidCI6IjFlNzI1MWY2LTk4MjItNDRjZi1hYjQyLWJkZmQ5ODJhMTFlMSIsImMiOjEwfQ%3D%3D
Customer Segmentation Analysis Report
This Customer Segmentation Analysis report, crafted from four years of sales data, delivers deep insights into sales trends, consumer segmentation, and Customer Lifetime Value (CLV), along with a detailed country-by-country breakdown. Inspired by "Annual Number of Deaths by Cause" from the theme gallery. If you find my report impressive, kindly do give it a Like! eyJrIjoiMzcxMTI3ZDgtZmRlNi00Y2UxLWE3ZmUtMDhmNDc3ZDQyZjQyIiwidCI6ImM0MjMzNjg5LTY2NWYtNDczMC1hNDc4LTQwNGE5MzExNjk5NyJ9
Power BI Python app build- BI Planner
Hi Microsoft Team. I am able to create a python streamlit app that can build the Power BI dashboard finance related on its own by just importing the datasets tables (large datasets )into the BI Planner web app. It automatically parse the data, do data column types transformation and fix data errors, and the then build semantic model, relationships and dax measures on its own and create a visual draft dashboard with visuals, It creates the visual story from the research packages finance related pbip repository and knowledge engine that I created using microsoft documents and finance and accounting knowledge agent . If I can get the Power BI developer access by Microsoft I can further enhance this BI Planner web app. This app currently does not use any AI API key at all and it is optional . Above is the architecture for the same. Happy to contribute in the development of Microsoft Product further and enhance the BI Intelligence tool ! I love Microsoft Products!
How are teams using AI agents to automate data science workflows?
I am exploring how AI agents can support modern data science workflows by automating repetitive tasks and improving collaboration between data teams. Some potential use cases: Automating data exploration and preprocessing steps Generating insights from datasets using natural language queries Assisting with feature engineering and model evaluation Triggering ML pipelines based on business events Monitoring model performance and identifying data quality issues I would like to understand how data science teams are combining Microsoft Fabric, notebooks, ML workflows, and AI agents in real-world projects. What approaches, tools, or architecture patterns are you using for AI-assisted data science workflows?Grão Raro: assinaturas de café especial
Relatório de acompanhamento de um clube de assinatura de cafés especiais. A Grão Raro é uma empresa fictícia e todos os dados são fictícios, criados para este portfólio. O relatório foi pensado para quem gerencia a operação de assinaturas: receita, base de assinantes, cancelamentos e entregas, um mês de cada vez, com comparação contra o mês anterior. Visão geral: o resumo do mês. Receita recorrente, assinantes ativos, novas assinaturas e taxa de cancelamento, com a receita por mês e plano, o saldo semanal de entradas e saídas, os cafés mais pedidos, o mapa de assinantes por estado e a preferência de torra. Manual do relatório: o que cada página mostra e como usar cada controle. As páginas Assinantes, Cancelamentos e Pedidos e entregas estão em construção e aparecem no menu com cadeado. Recursos de experiência de uso: Central de ajuda: uma janela de conversa, aberta pelo menu, que simula uma assistente de IA dentro do relatório. A pessoa digita uma pergunta em linguagem natural (“quanto faturou em março?”, “qual café mais vende no Sul?”, “quantos cancelaram por região?”) ou escolhe uma sugestão por assunto, e recebe o número, a comparação, um mini gráfico de barras e, quando cabe, a foto do café. Responde para qualquer mês, por café, torra, região, plano e canal. A conversa é mantida ao trocar de página, e há botões de nova conversa, copiar e avaliar a resposta. Filtros: reunidos em um painel próprio, em botões com estado de hover, com contador de filtros ativos e Limpar filtros. Manual e tour guiado: a página Manual do relatório descreve cada página e cada controle, e o tour “Como usar” apresenta os recursos passo a passo. Acessibilidade: contraste no nível AA em todos os textos, e todos os indicadores batem com os visuais de onde saem.Enable Direct Addition of Fabric Data Agents to Microsoft 365 Copilot
Please enable users to add and invoke a Fabric Data Agent directly as a specialized agent in a Microsoft 365 Copilot multi-agent solution. In our current setup, we can’t add a Fabric Data Agent directly to M365 Copilot, so we connect to it through Fabric IQ Data MCP. This approach raises a concern that we need to separately verify whether requests are routed to and handled by the intended Fabric Data Agent. That additional validation makes configuring and testing the multi-agent solution more complex. We request the ability to select and add a Fabric Data Agent directly as a subagent in the M365 Copilot multi-agent configuration, allowing the parent agent to delegate tasks to it directly. This would reduce the additional validation required for the intermediate MCP connection and make the multi-agent setup simpler and more transparent. This request is not a report of a defect in Fabric IQ Data MCP; it is a request for direct Fabric Data Agent integration in M365 Copilot. We also request that the Fabric Data Agent’s existing authentication, data-access permissions, and security policies continue to be enforced when using this direct integration. References: Consume a data agent in Microsoft Copilot Studio (preview) Consume a Fabric data agent as a tool in Microsoft Copilot StudioTFPUG Oct 2026 Event: FabCon Data Eng Updates, Osmos AI & Medallion Architecture
Please read the registration instructions very carefully to make sure you can attend the event: You must register on the event page: TFPUG Oct 2026 Event: FabCon Data Eng Updates, Osmos AI & Medallion Architecture | Microsoft Fabric Community Complete the mandatory registration form required by the event host: Toronto Fabric & Power BI User Group Oct 2026 Event Registration Form (Please note that seating is limited and priority will be given only to attendees who complete this additional registration form) Register only if you are confident you can attend, as no-shows may prevent others from participating and result in wasted catering and event resources. Please arrive on time. We will begin promptly at 5:30 PM sharp and have a full agenda planned for the evening. We are excited to have our Oct 2026 event sponsored by Protiviti Canada and hosted at Robert Half Toronto Office. Join our learning session and live demo focused on Fabric Data Engineering, Osmos AI and Medallion Architecture, where you’ll learn more about those topics. In this event we are privileged to have 2 main speakers and one demo from one of our user group community members. Agenda: 5:30 - Doors Open 5:45 - Welcome and Intro | Ashraf Ghonaim and Haniel Croitoru 6:00 - FabCon Data Engineering Updates and Osmos AI | Anuyogam Venkataraman 6:45 - Building Trusted Analytics with Microsoft Fabric: A Practical Guide to Medallion Architecture | Yashvardhan Vashistha 7:30 - Empowering Executive Decision-Making with Power BI Copilot | Sudhir Mehta 8:00 - Networking Snack 8:30 - Closing Anuyogam Venkataraman, Senior Program Manager - Microsoft | FabCon Data Engineering Updates and Osmos AI: A valuable session covering the latest FabCon Data Engineering updates and the innovative capabilities of Osmos AI. The discussion provided insights into emerging trends, new features, and practical applications that can help organizations enhance their data engineering strategies and accelerate AI-driven innovation. Yashvardhan Vashistha, Manager - Technology Consulting at Protiviti | Building Trusted Analytics with Microsoft Fabric: A Practical Guide to Medallion Architecture: Organizations often struggle with inconsistent data quality, duplicate transformations, and a lack of trust in reporting. Microsoft Fabric addresses these challenges through Medallion Architecture, a modern data design approach that organizes data into Bronze, Silver, and Gold layers. This presentation explores how raw source data is ingested into a Bronze Lakehouse, transformed and validated in the Silver layer, and ultimately modeled into business-ready Gold datasets that support reporting, analytics, and AI initiatives. Sudhir Mehta, Manager at Tredence, an active member of our user group community, demonstrates "Empowering Executive Decision-Making with Power BI Copilot." This session will showcase how Power BI Copilot transforms complex data into clear, actionable insights, enabling executives to identify trends, uncover opportunities, and make faster, more informed decisions through AI-powered analytics. Learn practical ways leaders can leverage Copilot to enhance strategic decision-making and drive better business outcomes. Looking forward to seeing you all there at 5:30 pm sharp. TFPUG Team Toronto Fabric & Power BI User Group | Microsoft Fabric Community Toronto Fabric & Power BI User Group | Meetup Toronto Fabric & Power BI User Group | LinkedIn
