Across industry sectors, both management and leaders see a yawning gap between the promised and delivered impact of data science projects and wonder why the discrepancy exists. It's simple, really. Companies rely on highly skilled and expensive data scientists to help them build predictive capabilities into their products and workflows, but they often think the data science team alone can lead the change.
This report examines issues from several conversations the authors held with data science teams across industries, as well as those issues they've witnessed in their own experience as builders and leaders. Among their findings, the authors agreed that to shorten the production process, lower overhead, and reduce risk, organizations need a comprehensive understanding of how to build AI in a repeatable fashion.
Technologists John J. Thomas, Paco Nathan, and William Roberts show data scientists how an organization and its technology work together to support their mission. Leaders of data science teams will examine how their organizations can transparently and seamlessly facilitate the delivery of data products. And business leaders will learn the value, both realized and potential, of introducing data science expertise in their organizations.
Table of contents
1. Current View and Challenges of AI Adoption
- Challenges for Business Stakeholders
- Challenges for Technical Stakeholders
- A Case Study in Navigating Challenges
- The Challenge of Trusted AI
- 2. Personas and Effective Communication Among Them
- 3. Design Thinking
- 4. Stages of the AI Life Cycle
- 5. AI Center of Excellence
6. Case Studies in Operationalizing AI
- Red Bull: Sandcastle—Log Cabin—Castle
- Capital One: Model as a Service for Real-Time Decisioning
- Wunderman Thompson: AI Factory Brings Solutions to Clients at Scale
- 7. Conclusion
- Title: Operationalizing AI
- Release date: March 2021
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781098101312
You might also like
51+ hours of video instruction. Overview The professional programmer’s Deitel® video guide to Python development with …
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. …
Clean Code: A Handbook of Agile Software Craftsmanship
Even bad code can function. But if code isn't clean, it can bring a development organization …
Data Science from Scratch, 2nd Edition
To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, …