Enterprise IT - AI Lifecycle Management Guide

Artificial Intelligence is rapidly being integrated into our daily workflows, business strategies, and customer experiences.

This book is a technology-agnostic primer for both business and technical decision-makers to understand the high-level structure of AI projects in the enterprise.

Are you a technology executive, digital transformation leader, product manager, data scientist, machine learning engineer, software engineer, or IT architect?

This book will help you identify the key steps for:

• Data Quality
• Data Annotation
• Synthetic Training Data
• Feature Engineering
• ML Platforms
• Version Control
• Model Risk
• Model Deployment
• Model Monitoring
• and Resource Optimization

You'll learn:

1. Key deliverables for each phase
2. Artifacts that you'll need to produce
3. Key concepts to understand
4. How to measure success for the individual phases of the AI Lifecycle Management Process
   

Get started learning a systematic approach to managing AI projects in the enterprise.