Microsoft Fabric Updates Blog

Copilot in Fabric (preview) is available worldwide 

During November 2023, we unveiled the public preview of Copilot in Microsoft Fabric. This preview includes Copilot for Power BI, Data Factory and Data Science & Data Engineering. Since then, we have been gradually rolling this feature out and today, we are excited to announce that our Copilot preview is now available to all customers! With the Copilot preview, Microsoft … Continue reading “Copilot in Fabric (preview) is available worldwide “

Using Microsoft Fabric’s Lakehouse Data and prompt flow in Azure Machine Learning Service to create RAG applications

Microsoft Fabric’s Lakehouse helps us better unified management of enterprise-level data environments. In the process of transforming to AI, we cannot do without the assistance of these enterprise data. In my previous blog, I mentioned how to build RAG applications based on data in the Microsoft Fabric environment. In this post, I will introduce how … Continue reading “Using Microsoft Fabric’s Lakehouse Data and prompt flow in Azure Machine Learning Service to create RAG applications”

Prebuilt Azure AI services in Fabric

During the recent Ignite 2023 event, we announced the public preview of prebuilt AI services in Fabric. This integration with Azure AI services, formerly known as Azure Cognitive Services, allows for easy enhancement of data with prebuilt AI models without any prerequisites.  Using AI services in Fabric has never been easier! In the past, you … Continue reading “Prebuilt Azure AI services in Fabric”

Semantic Link: OneLake integrated Semantic Models

Semantic Link adds support for the recently released OneLake integrated semantic models! You can now directly access data using your semantic model’s name via OneLake using the read_table function and the new mode parameter set to onelake. This approach ensures that no load is placed on Analysis Services, enabling efficient data retrieval and avoids putting … Continue reading “Semantic Link: OneLake integrated Semantic Models”

Semantic Link: Data validation using Great Expectations

Great Expectations Open Source(GX OSS) is a popular Python library that provides a framework for describing and validating the acceptable state of data. It helps data engineers and data scientists ensure that their data meets specific quality standards before using it for analysis, machine learning, or other data-driven tasks. With the recent integration of Microsoft … Continue reading “Semantic Link: Data validation using Great Expectations”