Operating AI: Bridging the Gap Between Technology and Business

Description

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 areas such as; data mesh, data fabric, aspects of security, data privacy, data rights and IPR related to data and AI models. In the book, you’ll also discover: How to reduce the risk of entering bias in our artificial intelligence solutions and how to approach explainable AI (XAI)The importance of efficient and reproduceable data pipelines, including how to manage your company's dataAn 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 using CI/CD/CT techniques, that generates value in the real worldKey competences and toolsets in AI development, deployment and operationsWhat to consider when operating different types of AI business models 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 Title Page Copyright Page About the Author About the Technical Editor Acknowledgments Contents at a Glance Contents 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 Applying an Operational Mind-set from the Start The Operational Challenge Strategy, People, and Technology Considerations Strategic Success Factors in Operating AI People and Mind-sets The Technology Perspective Chapter 2 Data Engineering Focused on AI Know Your Data Know the Data Structure Know the Data Records Know the Business Data Oddities Know the Data Origin Know the Data Collection Scope The Data Pipeline Types of Data Pipeline Solutions Data Quality in Data Pipelines The Data Quality Approach in AI/ML Scaling Data for AI Key Capabilities for Scaling Data Introducing a Data Mesh When You Have No Data The Role of a Data Fabric Why a Data Fabric Matters in AI/ML Key Competences and Skillsets in Data Engineering Chapter 3 Embracing MLOps MLOps as a Concept From ML Models to ML Pipelines The ML Pipeline Adopt a Continuous Learning Approach The Maturity of Your AI/ML Capability Level 0—Model Focus and No MLOps Level 1—Pipelines Rather than Models Level 2—Leveraging Continuous Learning The Model Training Environment Enabling ML Experimentation Using a Simulator for Model Training Environmental Impact of Training AI Models Considering the AI/ML Functional Technology Stack Key Competences and Toolsets in MLOps Clarifying Similarities and Differences MLOps Toolsets Chapter 4 Deployment with AI Operations in Mind Model Serving in Practice Feature Stores Deploying, Serving, and Inferencing Models at Scale The ML Inference Pipeline Model Serving Architecture Components Considerations Regarding Toolsets for Model Serving The Industrialization of AI The Importance of a Cultural Shift Chapter 5 Operating AI Is Different from Operating Software Model Monitoring Ensuring Efficient ML Model Monitoring Model Scoring in Production Retraining in Production Using Continuous Training Data Aspects Related to Model Retraining Understanding Different Retraining Techniques Deployment after Retraining Disadvantages of Retraining Models Frequently Diagnosing and Managing Model Performance Issues in Operations Issues with Data Processing Issues with Data Schema Change Data Loss at the Source Models Are Broken Upstream Monitoring Data Quality and Integrity Monitoring the Model Calls Monitoring the Data Schema Detecting Any Missing Data Validating the Feature Values Monitor the Feature Processing Model Monitoring for Stakeholders Ensuring Stakeholder Collaboration for Model Success Toolsets for Model Monitoring in Production Chapter 6 AI Is All About Trust Anonymizing Data Data Anonymization Techniques Pros and Cons of Data Anonymization Explainable AI Complex AI Models Are Harder to Understand What Is Interpretability? The Need for Interpretability in Different Phases Reducing Bias in Practice Rights to the Data and AI Models Data Ownership Who Owns What in a Trained AI Model? Balancing the IP Approach for AI Models The Role of AI Model Training Addressing IP Ownership in AI Results 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 Productivity Reliability Risk People Top Management and AI Business Realization Measuring AI Business Value Measuring AI Value in Nonrevenue Terms Operating Different AI Business Models Operating Artificial Intelligence as a Service Operating Embedded AI Solutions Operating a Hybrid AI Business Model Index EULA