A practical guide to successfully adopting and operating AI at scale

Artificial Intelligence has become a key business enabler across more and more industries. Corporations are starting to view AI as a technology for future-proofing their business far beyond organizational efficiency. By embracing the full potential of AI, every company and organization in some sense becomes a technology company, whether or not that is the ambition. But are companies ready for this massive transformation?

In Operating AI, distinguished MSc. Director at Ericsson AB, Ulrika Jgare, explains why operating AI is different from operating software, and why it's not as easy as it may seem to effectively deploy and leverage business value from AI in the enterprise. To be successful, you can't only focus on getting the technical pieces right, but it's vital to approach AI from a business operations perspective. In other words, how your AI solution is intended to run in production needs to drive decisions and priorities throughout the AI lifecycle. By breaking down barriers between AI in development and AI in production, you enable the organization to quickly and seamlessly move AI models to production and efficiently operate increasing numbers of models on a continuous basis in a live setting.

The accomplished author walks you through how to reduce the risk of introducing unwarranted bias into your AI solutions and how to create efficient and reproduceable data pipelines using a Machine Learning Operations (MLOps) approach.

Operating AI is a one-stop resource to help technical and non-technical professionals understand the operational dimensions of the early decisions that will need to be made. It also includes explanations of how to:

• Develop a balanced approach to AI cross data engineering model development and operations
• Understand and address data privacy concerns in AI solutions
• Sort out IPR rights in data and AI models
• Gain trust in your AI solution through explainable AI
• Measure business value from AI
• Operate different AI business models

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 Jgare, 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.