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.

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.

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.