Building and Distributing Agentic AI Solutions
Description
Discover and navigate the essential tools and technologies that streamline the product-building journey, enabling you to bring your AI solution to the Azure marketplace faster and with greater impact.
This book begins with an overview of the AI and Generative AI landscape, covering the fundamentals of machine learning, data, and cost models. You will then go through market research essentials-validating ideas, analyzing competition, addressing ethical and compliance concerns, and framing pricing strategies. Further,you will delve into the practical steps of designing and building an AI-first product on Azure. Here, you will explore key design considerations such as responsible AI, domain-specific data, multi-modal interfaces, and protecting intellectual property. With hands-on insights into Azure OpenAI, Copilot Studio, and AI Foundry, the book walks through building, packaging, and operationalizing AI services. Finally, it covers the path to market-publishing on the Azure Marketplace, executing a go-to-market strategy, partnering with Microsoft, and scaling for global adoption.
What You Will Learn:
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The latest AI technologies from Microsoft and Open Source partners
Foundational concepts when designing agents and Knowledge Sources
Cost analysis and pricing strategies for AI products
Prototyping and integration with Microsoft Copilot Studio - the UI for AI
Deep dive into modern agentic technologies such as MCP, multi-agent collaboration, and orchestration framework patterns
Hands-on exercises with code samples and fully working concepts
Understanding Azure Marketplace requirements
Publishing to Azure Marketplace and Copilot Studio Marketplace
Who This Book Is For:
Project managers, software engineers, and Independent Software Vendors (ISVs)
More details
Persons
Julian Soh is a software engineer and a cloud architect with Microsoft, focusing in the areas of artificial intelligence and advanced analytics for independent software vendors (ISVs) who develop software solutions based on the Microsoft technology stack. Prior to his current role, Julian worked extensively in major public cloud initiatives, such as SaaS (Microsoft 365), IaaS/PaaS (Microsoft Azure), and hybrid private-public cloud implementations.
John Tidwell is Chief Technology Officer at YoungWilliams, where he leads a team responsible for the ongoing design, development, and deployment of PriyaT an innovative AI-driven government service solution which leverages Microsoft Azure's AI platform. With expertise in Azure cloud technologies, generative AI, DevOps, and Agile software development, John has guided numerous projects from concept to production. His technical leadership includes architecting scalable systems, driving strategic cloud adoption, and mentoring development teams to achieve excellence. John holds multiple Azure certifications, including Azure AI, and is actively pursuing a Master of Science in Computer Science specializing in Artificial Intelligence and Machine Learning at Georgia Tech.
Priyanshi Singh is a senior artificial intelligence and machine learning technical specialist at Microsoft, specializing in designing end-to-end cloud solutions that leverage generative AI models and AI implementation best practices. She holds a master's degree in data science from New York University and has a robust background as a data scientist, focusing on machine learning techniques for predictive analytics, computer vision, and natural language processing. Priyanshi is dedicated to helping the public sector and independent software vendors (ISVs) transform citizen services through artificial intelligence. She has been recognized as Microsoft's FY24 State and Local Government Pinnacle Winner for her exceptional contributions to AI adoption and the growth of Azure business. Additionally, Priyanshi is a sports enthusiast, excelling in badminton and enjoying golf and billiards.
Content
Chapter 1: The AI Landscape.- Chapter 2: Envisioning the future with AI (20 pages).- Chapter 3: Market Research (30 pages).- Chapter 4: Designing your AI-first product.- Chapter 5: Building your AI product or service.- Chapter 6: Packaging and Operationalizing your AI product.- Chapter 7: Publishing on Azure Marketplace.- Chapter 8: Go-to-Market execution.- Chapter 9: Measuring success and scaling for growth.