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

This article serves as a comprehensive guide and a centralized resource for professionals venturing into the world of artificial intelligence with Azure. It aims to demystify the process of AI project development and provide a smooth transition of AI workloads into a production environment. This guide is tailored not only for technical practitioners but also for business leaders and project managers, offering a roadmap that spans from conceptualizing AI solutions to their full-scale deployment. By consolidating Azure AI information and tools, we aim to make the journey of AI integration and application more accessible and efficient for a diverse range of audiences.

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

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