Microsoft Mechanics Podcast

Check out new AI integrations for your Azure SQL databases. With Retrieval Augmented Generation, you can bridge structured data with generative AI, enhancing natural language queries across applications. With advanced vector-based semantic search, discover precise insights tailored to your data, while Copilot in Azure streamlines troubleshooting and T-SQL query authoring. Optimize workflows, personalize responses, and unlock new levels of efficiency in SQL-driven AI applications.

Accelerate performance troubleshooting and complex query authoring tasks with Copilot in Azure. Quickly diagnose database issues and receive expert recommendations for optimization, ensuring optimal performance and reliability. Seamlessly traverse hierarchies within tables and generate intricate queries with ease, saving time and resources.

Bob Ward, Azure Principal Architect, shows how to unleash the full potential of your SQL data, driving innovation and intelligence across your applications.

► QUICK LINKS: 00:00 - AI and Azure SQL 01:40 - Using T-SQL for search 02:30 - Using Azure AI Search 03:17 - Vector embeddings and skillsets 04:08 - Connect your SQL data to an AI app 05:44 - Test it in Azure OpenAI Studio playground 07:22 - Combine native JSON data type in SQL 08:30 - Hybrid search 09:56 - Copilot in Azure: Performance troubleshooting 11:11 - Copilot in Azure: Query authoring 12:24 - Permissions 12:40 - Wrap up

► Link References

For building AI apps, check out https://aka.ms/sqlai 

Try out new copilot experiences at https://aka.ms/sqlcopilot

► Unfamiliar with Microsoft Mechanics? 

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Bring your MySQL workloads to run on Azure. Azure Database for MySQL - Flexible Server offers a powerful, fully managed solution for MySQL workloads, providing unique platform-level optimizations that significantly enhance connection scaling and cost performance. Ensure high efficiency and reduced latency for mission-critical applications for up to twice the performance of other offerings. The integration with Azure OpenAI Service and Azure AI Search further extends its capabilities, enabling intelligent vector-based search and generative AI responses for more accurate and relevant user queries.

Join Parikshit Savjani, Azure Database for MySQL Principal Group PM, shares how Azure Database for MySQL - Flexible Server transforms traditional MySQL applications into high-performance, intelligent systems by combining the scalability and security of Azure with advanced AI-driven insights.

► QUICK LINKS: 00:00 - Run MySQL on Azure 00:56 - Run your LAMP stack on Azure 01:24 - Accelerated Logs capability 03:02 - Vector-based search and generative AI 04:02 - Leverage Azure AI Search & Azure OpenAI services 04:29 - Establish database connection 05:52 - Build vector index 07:04 - Create an Indexer 07:32 - Semantic Search 07:54 - Test it in the Azure AI playground 08:49 - Wrap up

► Link References

 Check out https://aka.ms/mysql-resources 

 Join the community for Azure Database for MySQL at https://aka.ms/mysql-contributors

► Unfamiliar with Microsoft Mechanics? 

As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft.

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Use your files, emails, chats and meeting information automatically or with direct references using Copilot prompts in Microsoft 365 apps. You don't need to copy information then paste it into a browser tool or upload work files and images into other generative AI experiences. Instead, directly from Teams, Word, PowerPoint and other Microsoft 365 apps, you can leverage not only your opened file, email, chat or meeting, but also other work data that you have permissions to in SharePoint and OneDrive. Copilot retrieves and synthesizes information across emails, chats, and files, so you can generate presentations, respond to emails, and catch up on meetings. The Copilot process also follows your organization's policies for data protection and compliance, eliminating the need and added risk of bringing your work data to those tools.

Mary David Pasch, Principal Product Manager from the Copilot Platform team, joins Jeremy Chapman to share how Copilot enhances collaboration and efficiency, making complex tasks like document creation and information retrieval effortless and secure.

► QUICK LINKS: 00:00 - Leverage your work data with Copilot 00:51 - How Copilot augments your prompts 02:22 - Automatically works across your files 03:03 - Meeting recaps with Copilot in Teams 03:59 - Meeting summaries with Copilot in Teams 04:57 - Incorporate specific info from work data 06:53 - Convert written docs to PowerPoint presentations 09:07 - Recommended prompts 10:29 - Standalone Copilot chat experience 12:11 - Stay within your Microsoft 365 data security boundary 12:39 - Wrap up

► Link References For more go to Copilot Lab at https://copilot.cloud.microsoft 

Watch our full series on Microsoft Mechanics at https://aka.ms/M365CopilotMechanics 

► Unfamiliar with Microsoft Mechanics? 

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Improve search capabilities for your PostgreSQL-backed applications using vector search and embeddings generated in under 10 milliseconds without sending data outside your PostgreSQL instance. Integrate real-time translation, sentiment analysis, and advanced AI functionalities securely within your database environment with Azure Local AI and Azure AI Service. Combine the Azure Local AI extension with the Azure AI extension to maximize the potential of AI-driven features in your applications, such as semantic search and real-time data translation, all while maintaining data security and efficiency.

Joshua Johnson, Principal Technical PM for Azure Database for PostgreSQL, demonstrates how you can reduce latency and ensure predictable performance by running locally deployed models, making it ideal for highly transactional applications.

► QUICK LINKS: 00:00 - Improve search for PostgreSQL 01:21 - Increased speed 02:47 - Plain text descriptive query 03:20 - Improve search results 04:57 - Semantic search with vector embeddings 06:10 - Test it out 06:41 - Azure local AI extension with Azure AI Service 07:39 - Wrap up

► Link References

Check out our previous episode on Azure AI extension at https://aka.ms/PGAIMechanics 

Get started with Azure Database for PostgreSQL - Flexible Server at https://aka.ms/postgresql 

To stay current with all the updates, check out our blog at https://aka.ms/azurepostgresblog

► Unfamiliar with Microsoft Mechanics? 

As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft.

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Ensure high-accuracy, efficient vector search at massive scale with Azure Cosmos DB. Leveraging Microsoft’s DiskANN, more IO traffic moves to disk to maximize storage capacity and enable high-speed similarity searches across all data, reducing memory dependency. This technology, powering global services like Microsoft 365, is now integrated into Azure Cosmos DB, enabling developers to build scalable, high-performance applications with built-in vector search, real-time fraud detection, and robust multi-tenancy support.

Join Kirill Gavrylyuk, VP for Azure Cosmos DB, as he shares how Azure Cosmos DB with DiskANN offers unparalleled speed, efficiency, and accuracy, making it the ideal solution for modern AI-driven applications.

► QUICK LINKS: 00:00 - Latest Cosmos DB optimizations with DiskANN 02:09 - Where DiskANN approach is beneficial 04:07 - Efficient querying 06:02 - DiskANN compared to HNSW 07:41 - Integrate DiskANN into a new or existing app 08:39 - Real-time transactional AI scenario 09:29 - Building a fraud detection sample app 10:59 - Vectorize transactions for anomaly detection 12:49 - Scaling to address high levels of traffic 14:05 - Manage multi-tenancy 15:35 - Wrap up

► Link References

Check out https://aka.ms/DiskANNCosmosDB

Try out apps at https://aka.ms/DiskANNCosmosDBSamples

► Unfamiliar with Microsoft Mechanics? 

As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft.

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Quickly spot real-time indicators of issues as they unfold, without the need to poll or manually monitor changes in your data and without writing a single line of code. That’s what the new Real-Time Intelligence service in Microsoft Fabric is all about. It extends Microsoft Fabric to the world of streaming data across your IoT and operational systems. Whether you are a data analyst or business user, you can easily explore high-granularity, high-volume data and spot issues before they impact your business. And as a Data engineer, you can more easily track system level changes across your data estate to manage and improve your pipelines.

Courtney Berg, from the Microsoft Fabric product team, joins Jeremy Chapman to explore all of the updates, explain how Real-Time Intelligence adds to what was possible with Data Activator and Microsoft Synapse Real-Time Analytics and demonstrates how this would work to derive insights and take actions automatically in a scenario with multiple live data streams across different data types.

► QUICK LINKS: 00:00 - Real-Time Intelligence 00:54 - How it’s different 02:07 - Eventstream and Real-Time Hub 03:27 - Synapse Real-Time Analytics 04:03 - See it in action 05:25 - Use Copilot to stay updated 06:28 - Filter data and set up alerts 08:25 - Sophisticated Logic and Data Integration 09:48 - Data integration to Real-Time Hub 11:23 - Workflow automation 12:18 - System events 13:30 - Additional areas of use for Real-Time Intelligence 14:02 - Wrap up

► Link References

Check out Real-Time Intelligence at https://aka.ms/RealTimeIntelligence

For all things Microsoft Fabric, go to https://microsoft.com/fabric

► Unfamiliar with Microsoft Mechanics? 

As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft.

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Optimize the sales process by integrating internal and external data with Copilot for Sales. Create enriched email summaries, customized meeting prep documents, and actionable meeting recaps to improve customer interactions. Copilot uses AI to generate insights and suggest actions based on comprehensive data, ensuring sales teams have all the necessary information at their fingertips. Create custom AI experiences using Copilot Studio with zero code. By adding Copilot extensions, users can connect to external data sources and build tailored AI skills.

Eric Boocock, Copilot for Sales Principal Program Manager, shares how Copilot for Sales works and how to build Copilot extensions to connect with other systems for personalized experiences.

► QUICK LINKS: 00:00 - Copilot for Sales 00:36 - Baseline experiences 02:13 - Bring in external data 03:26 - Use Copilot to respond to emails 03:54 - Meeting prep and meeting recap 04:52 - Security and Responsible AI 06:27 - Copilot extensions 09:09 - Publish Copilot extensions 09:40 - Wrap up

► Link References

Check out https://aka.ms/CopilotforSalesExtensibility 

Stay updated on monthly releases at https://aka.ms/CopilotforSalesUpdates

► Unfamiliar with Microsoft Mechanics? 

As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft.

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Now you can build your own highly specialized copilot using Microsoft Copilot Studio or Visual Studio Code. See how you can tailor Copilot for Microsoft 365 experiences to focus on specific tasks and use external data integration through Copilot extensions. Enhance productivity by leveraging large language models and Microsoft Graph for personalized responses. With plugins and connectors, extend Copilot's capabilities to streamline tasks and automate actions, all while maintaining context and boosting efficiency.

Abram Jackson, Principal Product Manager for Microsoft 365 shows how you can get started building Copilot extensions.

► QUICK LINKS: 00:00 - Copilot extensions 01:08 - Types of Copilot extensions 01:59 - Microsoft Graph connectors 03:20 - Plugins 04:14 - Specialized copilot 04:54 - Multiple customized copilots working together 05:42 - How to build Copilot extensions 06:45 - Plug into data-external or inside of Microsoft 365 07:31 - IDE/Visual Studio Code 08:57 - Wrap up

► Link References

To get started, check out https://copilotstudio.microsoft.com

► Unfamiliar with Microsoft Mechanics? 

As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft.

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Microsoft has built the world’s largest cloud-based AI supercomputer that is already exponentially bigger than it was just 6 months ago, paving the way for a future with agentic systems.

For example, its AI infrastructure is capable of training and inferencing the most sophisticated large language models at massive scale on Azure. In parallel, Microsoft is also developing some of the most compact small language models with Phi-3, capable of running offline on your mobile phone. 

Watch Azure CTO and Microsoft Technical Fellow Mark Russinovich demonstrate this hands-on and go into the mechanics of how Microsoft is able to optimize and deliver performance with its AI infrastructure to run AI workloads of any size efficiently on a global scale. 

This includes a look at: how it designs its AI systems to take a modular and scalable approach to running a diverse set of hardware including the latest GPUs from industry leaders as well as Microsoft’s own silicon innovations; the work to develop a common interoperability layer for GPUs and AI accelerators, and its work to develop its own state-of-the-art AI-optimized hardware and software architecture to run its own commercial services like Microsoft Copilot and more.

► QUICK LINKS: 00:00 - AI Supercomputer 01:51 - Azure optimized for inference 02:41 - Small Language Models (SLMs) 03:31 - Phi-3 family of SLMs 05:03 - How to choose between SLM & LLM 06:04 - Large Language Models (LLMs) 07:47 - Our work with Maia 08:52 - Liquid cooled system for AI workloads 09:48 - Sustainability commitments 10:15 - Move between GPUs without rewriting code or building custom kernels. 11:22 - Run the same underlying models and code on Maia silicon 12:30 - Swap LLMs or specialized models with others13:38 - Fine-tune an LLM 14:15 - Wrap up

► Unfamiliar with Microsoft Mechanics? 

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Use Postgres as a managed service on Azure. As you build generative AI apps, explore advantages of Azure Database for Postgres Flexible Server such as integration with Azure AI services, as well as extensibility and compatibility, integrated enterprise capabilities to protect data, and controls for managing business continuity.

Charles Feddersen, product team lead for Postgres on Azure, joins host Jeremy Chapman to share how Flexible Server is a complete Postgres platform for enterprise and developers.

► QUICK LINKS: 00:00 - Azure Database for PostgreSQL Flexible Server 00:51 - Microsoft and Postgres 01:40 - Open-source Postgres 03:18 - Vector embeddings for data 04:32 - How it works with an app 06:59 - Azure AI Vision 08:14 - Azure AI extension using PostgreSQL 09:37 - Text generation using Azure AI extension 10:30 - High availability and disaster recovery 12:45 - Wrap up

► Link References

Get started with the Azure Database for Postgres flexible server at https://aka.ms/postgresql

Stay current with all the updates at https://aka.ms/AzurePostgresBlog

► Unfamiliar with Microsoft Mechanics? 

As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft.

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#PostgreSQL #OpenSource #Azure #Database