AI Lifecycle Mastery: From Concept to Reality – Navigating Successful AI Deployments

Welcome to "AI Lifecycle Mastery: From Concept to Reality – Navigating Successful AI Deployments," a comprehensive guide designed to be a central resource for professionals venturing into the world of artificial intelligence with Azure. This guide is not just a brief overview but an extensive, carefully curated collection of chapters, each offering deep insights into a specific aspect of AI project development and deployment using Azure.

Who This Guide is For:

This guide is crafted for a diverse range of professionals, including:

• Technical practitioners seeking in-depth knowledge of AI integration.
• Business leaders looking for guidance on the strategic aspects of AI deployments.
• Project managers aiming for effective coordination and implementation of AI projects.

What You Will Find Inside:

Structured as a 15-chapter journey, this guide provides:

1. Detailed explorations of Azure's AI services, including practical applications and theoretical underpinnings.
2. A blend of technical guidelines and strategic advice, ensuring a holistic understanding of AI project development.
3. An array of resources, from step-by-step instructions to links for extended learning, catering to both technical and business-oriented readers.

Our Objective:

Our aim is to demystify the complexities of AI project development in Azure, offering a clear roadmap from conceptualizing AI solutions to their full-scale deployment. By consolidating Azure AI information and tools, we facilitate a smoother and more efficient integration of AI into your business operations.

How to Use This Guide:

• Navigating the Chapters: Follow the chapters sequentially for a structured learning path or navigate to specific topics of interest for targeted information.
• Hands-On Experience: Utilize the provided links and resources for practical application and deeper exploration.
• Engage with the Community: Benefit from the knowledge and experience of the contributors who are seasoned professionals in the field.

Table of Contents

1. Chapter 1: Setting Off: Understanding AI's Landscape
2. Chapter 2: Charting the Course: Ideation and Goal Setting
3. Chapter 3: Gathering Your Crew: Building the Right Team
4. Chapter 4: Mapping the Terrain: Data Management and Ethics
5. Chapter 5: Crafting the Vessel: Design and Development
6. Chapter 6: Testing the Waters: Testing and Iteration
7. Chapter 7: Navigating Rough Seas: Performance
8. Chapter 8: Securing the Cargo: Networking & Security
9. Chapter 9: Managing the Expedition: Cost Management/Optimization
10. Chapter 10: Weatherproofing the Journey: Reliability/High Availability
11. Chapter 11: Expanding Horizons: Scaling & Quota Management
12. Chapter 12: Keeping a Log: Observability
13. Chapter 13: Building for Everyone: Multitenant Architecture
14. Chapter 14: Arrival: Deployment Strategies
15. Chapter 15: Continuing the Voyage: Monitoring and Maintenance

Building and Previewing the Guide Locally

Assuming Ruby 3.3, Bundler, and Jekyll are installed on your machine, follow these steps to build and preview the guide locally.

Note

Jekyll is not officially supported for Windows. For more information, see "Jekyll on Windows" in the Jekyll documentation.

1. Change your working directory to the root of this repository.
2. Run bundle to install all necessary dependencies.
3. Run ./run.sh to build the guide site and start the Jekyll server.
4. Preview the guide at http://localhost:4000/AI-in-Production-Guide/.

The built site is stored in the directory _site.

Contributors

The content and resources in this guide have been curated by the following original contributors.

• Sofia Ferreira - Customer Engineer - Microsoft
• Yoav Dobrin - Principal Customer Engineer - Microsoft
• James Croft - Customer Engineer - Microsoft
• Olga Molocenco-Ciureanu - Customer Engineer - Microsoft