Operating AI: Bridging the Gap Between Technology and Business
- Length: 272 pages
- Edition: 1
- Language: English
- Publisher: Wiley
- Publication Date: 2022-05-24
- ISBN-10: 1119833191
- ISBN-13: 9781119833192
- Sales Rank: #1064769 (See Top 100 Books)
A holistic and real-world approach to operationalizing artificial intelligence in your company
In Operating AI, Director of Technology and Architecture at Ericsson AB, Ulrika Jägare, delivers an eye-opening new discussion of how to introduce your organization to artificial intelligence by balancing data engineering, model development, and AI operations. You’ll learn the importance of embracing an AI operational mindset to successfully operate AI and lead AI initiatives through the entire lifecycle, including key aspects of security, privacy, and data rights.
In the book, you’ll also discover:
- How to reduce the risk of entering bias in our artificial intelligence solutions
- The importance of efficient and reproduceable data pipelines, including how to manage your company’s data
- An operational perspective on the development of AI models using the MLOps (Machine Learning Operations) approach, including how to deploy, run and monitor models and ML pipelines in production, that generates value in the real world
With a strong emphasis on deployment and operations of trustworthy and reliable AI solutions that operate well in the real world—and not just the lab—Operating AI is a must-read for business leaders looking for ways to operationalize an AI business model that actually makes money, from the concept phase to running in a live production environment.
Cover Table of Contents Title Page Foreword Introduction What Does This Book Cover? How to Contact the Publisher How to Contact the Author CHAPTER 1: Balancing the AI Investment Defining AI and Related Concepts Operational Readiness and Why It Matters The Operational Challenge Strategy, People, and Technology Considerations CHAPTER 2: Data Engineering Focused on AI Know Your Data The Data Pipeline Scaling Data for AI The Role of a Data Fabric Key Competences and Skillsets in Data Engineering CHAPTER 3: Embracing MLOps MLOps as a Concept From ML Models to ML Pipelines Adopt a Continuous Learning Approach The Maturity of Your AI/ML Capability The Model Training Environment Considering the AI/ML Functional Technology Stack Key Competences and Toolsets in MLOps CHAPTER 4: Deployment with AI Operations in Mind Model Serving in Practice The ML Inference Pipeline The Industrialization of AI The Importance of a Cultural Shift CHAPTER 5: Operating AI Is Different from Operating Software Model Monitoring Model Scoring in Production Retraining in Production Using Continuous Training Diagnosing and Managing Model Performance Issues in Operations Model Monitoring for Stakeholders Toolsets for Model Monitoring in Production CHAPTER 6: AI Is All About Trust Anonymizing Data Explainable AI Reducing Bias in Practice Rights to the Data and AI Models Legal Aspects of AI Techniques Operational Governance of Data and AI CHAPTER 7: Achieving Business Value from AI The Challenge of Leveraging Value from AI Top Management and AI Business Realization Measuring AI Business Value Operating Different AI Business Models Index Copyright Dedication About the Author About the Technical Editor Acknowledgments End User License Agreement
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