Operating Ai
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Operating AI
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 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 using CI/CD/CT techniques, that generates value in the real world Key competences and toolsets in AI development, deployment and operations What 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.
Artificial Intelligence
Author: Kerrigan, Charles
language: en
Publisher: Edward Elgar Publishing
Release Date: 2022-03-17
This timely book provides an extensive overview and analysis of the law and regulation as it applies to the technology and uses of Artificial Intelligence (AI). It examines the human and ethical concerns associated with the technology, the history of AI and AI in commercial contexts.
Artificial Intelligence Products in the United States
AI product adoption is on the rise, and the market size is expanding. Specifically, data from the United States reveals: • The Artificial Intelligence market is projected to reach US$87.18bn in 2023. • The market is expected to show an annual growth rate (CAGR 2023-2030) of 15.36%, resulting in a market volume of US$237.10bn by 2030. • Globally, the largest market size will be in the United States (US$87.18bn in 2023). But which segment is growing the fastest? Which segment will likely dominate the future? What are the challenges? How will the competitive landscape evolve? Which is the least adopted AI product segment, and why? This research breaks down AI product segments, including AI Robotics, Autonomous & Sensor Technology, Computer Vision, Generative AI, Machine Learning, and Natural Language Processing. It provides answers using data and is well-suited for a portrait reading experience, helping you understand the market on the go. These findings are a valuable resource for businesses, policymakers, and stakeholders seeking to navigate and leverage the evolving AI landscape in the United States. Details: 50 Pages 20+ Charts Projections for the market size of each AI sector from 2023 to 2030 in the United States, including the Compound Annual Growth Rate (CAGR) for each sector. Data Sources: Mainly obtained from Statista Premium, which exceeded 3000 USD for the data alone. AI's Impact on Business: Gain a deep understanding of the positive impact of AI technologies on increasing revenue and reducing costs across various business functions. Table of Content: Chapter 1: Executive Summary Scope and Limitation Chapter 2: Market Overview Historical Development of AI Software Current Market Landscape Key Approaches in AI business Strategy Leading U.S. AI Product Companies AI Software Ecosystem Companies Chapter 3: AI Segments AI Robotics Autonomous & Sensor Technology Computer Vision Generative AI Machine Learning Natural Language Processing Chapter 4: Market Growth and Projections User Share in Generative AI Businesses User Segmentation in United States Generative AI Adoption Across U.S. Generations U.S. 2021 Public Opinion Segmentation in AI Control Factors Driving Market Growth Investment and Funding Trends Chapter 5: Market Challenges Ethical and Regulatory Challenges Data Privacy and Security Concerns Talent Shortage Chapter 6: Competitive Analysis Competitive Landscape, AI Robotics Competitive Landscape, Autonomous & Sensor Technology Competitive Landscape, Computer Vision Competitive Landscape, Generative AI Competitive Landscape, Machine Learning Competitive Landscape, Natural Language Processing Competitive Strategies in AI Business Against Industry Giants Chapter 7: Global AI Business Impact by Function Increased Revenue in 2021 Cost Reduction in 2022 Supply Chain Benefits 2022 Chapter 8: Breakthrough Technologies Artificial Emotional Intelligence Reinforcement Learning Deep Learning Sequential Learning Chapter 9: Conclusion Chapter 10: Appendices Methodology: Extensive Desk Research, emphasizing data collection primarily from statista.com