MCP & AI Unlocking Agentic Intelligence with a “USB-C Connector” for AI
MCP, or Model Context Protocol, is an open-source standard introduced by Anthropic in November 2024. It’s designed to create a unified bridge between AI models—especially large language models (LLMs)—and external systems like tools, databases, file repositories, and APIs. Think of MCP as the USB-C port for AI—just plug in, and the AI can access or drive external services without building unique integrations for each connection. Rather than coding separate connections for each model and tool, MCP uses a consistent, structured way for AI agents (MCP clients) to communicate with “MCP servers” that interface with external systems. https://dellenny.com/mcp-ai-unlocking-agentic-intelligence-with-a-usb-c-connector-for-ai/Exploring the Core Components of Microsoft Fabric A Unified Data Platform
As data continues to be the new oil, organizations are increasingly seeking robust platforms that can simplify and unify their data landscape. Enter Microsoft Fabric—a next-generation data platform introduced by Microsoft that brings together all the data and analytics tools needed in the modern enterprise, integrated into a single, SaaS-based solution. In this post, we’ll break down the key components of Microsoft Fabric, explain how they work together, and highlight why this platform is a game-changer for data professionals, developers, and decision-makers alike. https://dellenny.com/exploring-the-core-components-of-microsoft-fabric-a-unified-data-platform/Unlocking Innovation with Azure AI Foundry Agent Service
In today’s AI-driven landscape, the ability to build, orchestrate, and operationalize intelligent agents at scale is becoming increasingly critical for organizations seeking to leverage AI as a core capability. Microsoft’s Azure AI Foundry Agent Service, introduced as part of the Azure AI Studio ecosystem, is a game-changing platform designed to empower developers and enterprises to build sophisticated multi-agent AI systems with minimal friction. https://dellenny.com/unlocking-innovation-with-azure-ai-foundry-agent-service/Essentials to build and modernize AI applications on Azure
Need to confidently design AI applications? In this session we’ll explore the prescriptive guidance, resources and tools available within Azure Essentials to help you build and modernize reliable, secure AI applications. We will demo how to get started designing an AI workload using the AI assessment tool from the Azure Well-Architected Framework and then show how to build it using Azure AI Foundry. This session is part of Tech Accelerator: Mastering Azure and AI adoption. View the full agenda for more great sessions and insights.Govern, manage, and secure your AI deployments
Are you ready to adopt AI at scale? Explore the Azure Essentials AI adoption guidance and provide actionable steps and best practices to get your environment AI-ready. See how to make your AI workloads resilient and secure with solutions from Microsoft Defender for Cloud, Azure Purview, and the Azure Proactive Resiliency Library. This session is part of Tech Accelerator: Mastering Azure and AI adoption. View the full agenda for more great sessions and insights.Best practices for secure and reliable Azure projects
Join us for a conversation with senior Microsoft and AMD leaders as they discuss how organizations can securely and reliably migrate, modernize, and enhance existing deployments with Azure. Hear how Microsoft customers have leveraged Microsoft and AMD products and solutions to design, deploy, govern, manage their Azure workloads so they can drive ongoing performance and innovation in their organizations. Learn from the leaders about how you can best maximize your cloud and AI investment. This session is part of Tech Accelerator: Mastering Azure and AI adoption. Check out the full agenda for more great sessions and insights.Demystifying Gen AI Models - Transformers Architecture : 'Attention Is All You Need'
The Transformer architecture demonstrated that carefully designed attention mechanisms — without the need for sequential recurrence — could model language and sequences more effectively and efficiently. 1. Transformers Replace Recurrence Traditional models such as RNNs and LSTMs processed data sequentially. Transformers use self-attention mechanisms to process all tokens simultaneously, enabling parallelisation, faster training, and better handling of long-range dependencies. 2. Self-Attention is Central Each token considers (attends to) all other tokens to gather contextual information. Attention scores are calculated between every pair of input tokens, capturing relationships irrespective of their position. 3. Multi-Head Attention Enhances Learning Rather than relying on a single attention mechanism, the model utilises multiple attention heads. Each head independently learns different aspects of relationships (such as syntax or meaning). The outputs from all heads are then combined to produce richer representations. 4. Positional Encoding Introduced As there is no recurrence, positional information must be introduced manually. Positional encodings (using sine and cosine functions of varying frequencies) are added to input embeddings to maintain the order of the sequence. 5. Encoder-Decoder Structure The model is composed of two main parts: Encoder: A stack of layers that processes the input sequence. Decoder: A stack of layers that generates the output, one token at a time (whilst attending to the encoder outputs). 6. Layer Composition Each encoder and decoder layer includes: Multi-Head Self-Attention Feed-Forward Neural Network (applied to each token independently) Residual Connections and Layer Normalisation to stabilise training. 7. Scaled Dot-Product Attention Attention scores are calculated using dot products between queries and keys, scaled by the square root of the dimension to prevent excessively large values, before being passed through a softmax. 8. Simpler, Yet More Powerful Despite removing recurrence, the Transformer outperformed more complex architectures such as stacked LSTMs on translation tasks (for instance, English-German). Training is considerably quicker (thanks to parallelism), particularly on long sequences. 9. Key Achievement Transformers became the state-of-the-art model for many natural language processing tasks — paving the way for later innovations such as BERT, GPT, T5, and others. The latest breakthrough in generative AI models is owed to the development of the Transformer architecture. Transformers were introduced in the Attention is all you need paper by Vaswani, et al. from 2017.Safeguard AI applications with Microsoft Defender for Cloud
Generative AI applications drive innovation and efficiency but often face security risks like AI jailbreaks and data exfiltration. This can pose a serious threat to your organization's security and reputation. Learn how to prevent AI risks through robust AI-security posture management and discover effective strategies to detect and respond to AI threats. Enhance your understanding of AI security and protect your AI assets with Microsoft Defender for Cloud. This session is part of the Azure security and AI adoption Tech Accelerator. Increase your skills by checking out more sessions to help you plan, build, manage and optimize your Azure deployments and AI projects with a security-first mindset. Learn more with Microsoft Secure! Find solutions your organization can use to protect your data, defend against cyberthreats, and stay compliant.How to design and build secure AI projects
Given the complexities of AI integration, designing and building secure AI projects is critical. This session equips technical professionals with the knowledge and tools to create secure AI workloads. Attendees will learn how to incorporate Responsible AI and security-by-design principles from the Azure Well-Architected Framework, identify tradeoffs during design, and prioritize security. This session includes a live demo of the Azure Well-Architected Framework AI assessment, showcasing practical applications and best practices - ideal for anyone seeking to enhance their expertise in secure AI development. This session is part of the Azure security and AI adoption Tech Accelerator. Increase your skills by checking out more sessions to help you plan, build, manage and optimize your Azure deployments and AI projects with a security-first mindset. Learn more with Microsoft Secure! Find solutions your organization can use to protect your data, defend against cyberthreats, and stay compliant.How to secure your AI environment
Prioritizing security in AI projects is vital. This session covers Azure Essentials best practices for safeguarding AI Landing Zones, focusing on the infrastructure, data, and models for GenAI applications. Attendees will learn about integrating products and PaaS services like OpenAI, Azure API Management, and Machine Learning into a comprehensive architecture that shields the AI environment. The session will also feature lessons learned from the field and insights from Microsoft's internal best practices for securing AI deployments. This session is part of the Azure security and AI adoption Tech Accelerator. Increase your skills by checking out more sessions to help you plan, build, manage and optimize your Azure deployments and AI projects with a security-first mindset. Learn more with Microsoft Secure! Find solutions your organization can use to protect your data, defend against cyberthreats, and stay compliant.
