Machine Learning Times
Machine Learning Times
A University Curriculum Supplement to Teach a Business Framework for ML Deployment
    In 2023, as a visiting analytics professor...
The AI Playbook: Providing Important Reminders to Data Professionals
 Originally published in DATAVERSITY. This article reviews the new...
Decode the Algorithm: Navigate the World of Machine Learning in Business with ‘The AI ​​Playbook’
  This article reviews the new book, The AI Playbook, by...
To Deploy Machine Learning, You Must Manage Operational Change—Here Is How UPS Got It Right
 Originally published in Harvard Data Science Review. For more...

3 months ago
BizML: Bridging the Gap Between Data Science and Business


Eric Siegel, author of The AI Playbook, was interviewed by Pragmatic Data about a business paradigm to get machine learning deployed, bizML.  Click here to listen to the podcast episode

“There’s a gap between the technical side and the business side. You need to bridge that gap with a common understanding of what’s predicted, how well, and what’s done about it.”

– Eric Siegel

In this insightful Data Chats episode, we explore the critical intersection of business and machine learning with Eric Siegel, a renowned expert in the field. Eric’s insights offer a roadmap for organizations seeking to leverage the power of AI to drive their business forward.

Eric Siegel is the founder of Machine Learning Week, former Columbia professor, and bestselling author of his new release, “The AI Playbook: Mastering the Rare Art of Machine Learning Deployment.” He has a talent for bridging the gap between complex data science concepts and the practical application of machine learning in the business world.

Check out Eric’s new release, “The AI Playbook: Mastering the Rare Art of Machine Learning Deployment” here.

With his remarkable insights, Eric Siegel sits down with host, Chris Richardson, as he guides us through the exciting yet challenging landscape of AI’s role in business. They discuss:

  • Aligning with Business Objectives: Success comes from defining clear goals and understanding how AI can contribute to achieving them.
  • Measuring Deployment Success: Companies must define meaningful metrics that reflect the project’s impact on the business.
  • Human Involvement: While AI and ML hold great potential, there’s still a need for human involvement to validate AI-generated results and maintain quality control.
  • New Roles and Responsibilities: As AI’s role in business continues to evolve, new job titles and roles, such as machine learning business liaisons, are emerging.

Want to learn more from Eric? Tune in to his previous interview with us here.

Click here to listen to the podcast episode.

3 thoughts on “BizML: Bridging the Gap Between Data Science and Business

  1. I would like to know if there is a commercial specialization to sell machine learning products, without being a programmer and not necessarily being the internal salesman of the technology companies. It is clear that most of the companies have not taken this step and for sure there is a need for a facilitator or a bridge between the technology service companies and the companies that need to optimize their processes.

  2. Thank you for sharing this information about Eric Siegel’s interview on the Pragmatic Data podcast wordle and his insights into the intersection of business and machine learning.


Leave a Reply