• PAW Events
    • Machine Learning Week North America
    • Machine Learning Week Europe
    • PAW Business
      • North America
      • Europe
    • PAW Financial
      • North America
      • Europe
    • PAW Healthcare
      • North America
      • Europe
    • PAW Industry 4.0
      • North America
      • Europe
    • PAW Climate
      • North America
      • Europe
    • Deep Learning World
      • North America
      • Europe
    • Data Driven Government
      • Washington DC
    • Training Workshops
      • North America
      • Europe
    • Forthcoming Events
    • Previous Events
Predictive Analytics World

The machine learning blog from Eric Siegel, founder of Predictive Analytics World

  • Events
    • Machine Learning Week North America
    • Machine Learning Week Europe
    • PAW Business
      • North America
      • Europe
    • PAW Financial
      • North America
      • Europe
    • PAW Healthcare
      • North America
      • Europe
    • PAW Industry 4.0
      • North America
      • Europe
    • PAW Climate
      • North America
      • Europe
    • Deep Learning World
      • North America
      • Europe
    • Data Driven Government
      • Washington DC
    • Training Workshops
      • North America
      • Europe
    • Forthcoming Events
    • Previous Events
    • PAW Climate 2021
  • More info
    • About PAW
    • Call for Speakers
    • Sponsor
      • USA
      • Germany
    • Previous Events
    • Press Room
    • Contact
    • Sign Up for Event Notifications
  • Resources
    • The Machine Learning Times
    • The Predictive Analytics Guide
    • ML Training
    • Bestselling Book
    • Blog
  • Register
Select Page

Machine Learning’s Missing Link: Business Leadership

Mar 20, 2021 | 0 comments

Machine learning. Your team needs it, your boss demands it, and your career loves it. After all, LinkedIn places it as one of the top few “Skills Companies Need Most” and as the very top emerging job in the U.S.

But this number-crunching craze tends to, tragically, overlook one key point: Of all the ingredients that are key to success with machine learning, the one that’s most often missing isn’t about technology or data. It’s about leadership. Many business leaders do know that machine learning can’t succeed in optimizing operations without a proven management process guiding the project – but data scientists tend to focus on one thing and one thing only: the hands-on practice of analytics.

Now, it’s true that you learn best from doing – but the number crunching is only half of what needs to get done. There’s also a business-side leadership process critical to machine learning’s value-driven deployment, and data scientists must ramp up on it just as well as business leaders. Whether you’ll participate on the business or tech side of a machine learning project, the business-side skills of ML are essential, pertinent know-how. They’re needed in order to ensure the core technology works within – and successfully produces value for – business operations.

A main, central portion of my course “Machine Learning Leadership and Practice – End-to-End Mastery” addresses this need. First, allow me to tell you about the course: It will guide you and your team to lead or participate in the end-to-end implementation of machine learning. It’s an expansive curriculum that’s accessible to business-level learners and yet vital to techies as well. It covers both the state-of-the-art techniques and the business-side best practices.

By covering the business-side requirements, unlike most machine learning courses, this one prepares you to avoid the most common management mistake that derails machine learning projects: jumping straight into the number crunching before establishing and planning for a path to operational deployment.

In particular, the course includes three sub-courses, one entitled, “Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership”, which focuses entirely on the business side. After this sub-course, you will be able to:

– Lead ML: Manage a machine learning project, from the generation of predictive models to their launch.

– Apply ML: Identify the opportunities where machine learning can improve marketing, sales, financial credit scoring, insurance, fraud detection, and much more.

– Plan ML: Determine the way in which machine learning will be operationally integrated and deployed, and the staffing and data requirements to get there.

– Greenlight ML: Forecast the effectiveness of a machine learning project and then internally sell it, gaining buy-in from your colleagues.

– Prep data for ML: Oversee the data preparation, which is directly informed by business priorities.

– Evaluate ML: Report on the performance of predictive models in business terms, such as profit and ROI.

– Regulate ML: Manage ethical pitfalls, such as when predictive models reveal sensitive information about individuals, including whether they’re pregnant, will quit their job, or may be arrested.

The first module of this sub-course dives deeply into the business applications of machine learning – for marketing, financial services, fraud detection and more. We illustrate the value delivered for these domains by way of case studies and detailed examples. And we’ll precisely measure the performance of the predictive models themselves, focusing on model lift, a predictive multiplier that tells you the improvement achieved by a model.

The second module of this sub-course covers scoping, greenlighting, and managing machine learning initiatives. Launching machine learning is as much a management endeavor as a technical one – its success relies on a very particular business leadership practice. This module will demonstrate that practice, guiding you to lead the end-to-end implementation of machine learning. Here’s its outline of topics:

Leadership process: How to manage machine learning projects

  • Project management overview
  • The six steps for running a ML project
  • Running and iterating on the process steps
  • How long a machine learning project takes
  • Refining the prediction goal

Project scoping and greenlighting

  • Where to start – picking your first ML project
  • Strategic objectives and key performance indicators
  • Personnel – staffing your machine learning team
  • Sourcing the staff for a machine learning project
  • Greenlighting: Internally selling a machine learning initiative
  • More tips for getting the green light

And finally, the third module of this sub-course covers the data requirements – which needs very much to be informed by business-side considerations – and the fourth and last module covers more business metrics – including a fallacy about “high-accuracy” machine learning that spreads misinformation all across the Internet – and tackles some critical, alarming topics in machine learning ethics.

Those who are more a hands-on technical quant than a business leader will find this curriculum to be a rare opportunity to ramp up on the business side, since technical machine learning trainings don’t usually go there. But data wonks must know this: The soft skills are often the hard ones.

To learn more, check out the details of “Machine Learning Leadership and Practice – End-to-End Mastery.”

About the Author

Eric Siegel, Ph.D., is a leading consultant and former Columbia University professor who makes machine learning understandable and captivating. He is the founder of the Predictive Analytics World and Deep Learning World conference series, which have served more than 17,000 attendees since 2009, the instructor of the acclaimed online course “Machine Learning Leadership and Practice – End-to-End Mastery”, a popular speaker who’s been commissioned for more than 110 keynote addresses, and executive editor of The Machine Learning Times. He authored the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, which has been used in courses at more than 35 universities, and he won teaching awards when he was a professor at Columbia University, where he sang educational songs to his students. Eric also publishes op-eds on analytics and social justice. Follow him at @predictanalytic.

Recent Posts

  • Real-Time Machine Learning: Why It’s Vital and How to Do It
  • Liftoff: The Basics of Predictive Model Deployment
  • Hot Video: More Accuracy Fallacies – Predicting Criminality and Psychosis
  • The Precondition for Machine Learning Success: Bridge the Quant/Business Culture Gap
  • Hot Video: The Accuracy Fallacy – Bogus Machine Learning Results

Archives

  • June 2021
  • May 2021
  • April 2021
  • March 2021
  • October 2020
  • September 2020
  • August 2020
  • October 2019
  • June 2019
  • May 2019
  • February 2019
  • January 2019
  • December 2018
  • November 2018
  • October 2018
  • September 2018
  • May 2018
  • March 2018
  • February 2018
  • January 2018
  • October 2017
  • September 2017
  • July 2017
  • June 2017
  • May 2017
  • April 2017
  • March 2017
  • February 2017
  • January 2017
  • December 2016
  • September 2016
  • August 2016
  • July 2016
  • June 2016
  • May 2016
  • April 2016
  • March 2016
  • February 2016
  • January 2016
  • December 2015
  • November 2015
  • September 2015
  • August 2015
  • July 2015
  • May 2015
  • April 2015
  • March 2015
  • February 2015
  • January 2015
  • December 2014
  • November 2014
  • September 2014
  • August 2014
  • July 2014
  • May 2014
  • March 2014
  • February 2014
  • January 2014
  • December 2013
  • November 2013
  • October 2013
  • September 2013
  • August 2013
  • July 2013
  • June 2013
  • May 2013
  • March 2013
  • February 2013
  • January 2013
  • December 2012
  • November 2012
  • October 2012
  • September 2012
  • August 2012
  • July 2012
  • June 2012
  • May 2012
  • April 2012
  • March 2012
  • February 2012
  • January 2012
  • December 2011
  • November 2011
  • October 2011
  • September 2011
  • August 2011
  • July 2011
  • June 2011
  • May 2011
  • April 2011
  • March 2011
  • February 2011
  • January 2011
  • December 2010
  • November 2010
  • October 2010
  • September 2010
  • August 2010
  • July 2010
  • June 2010
  • May 2010
  • April 2010
  • March 2010
  • February 2010
  • January 2010
  • December 2009
  • November 2009
  • October 2009
  • September 2009
  • August 2009
  • July 2009
  • June 2009
  • May 2009
  • April 2009
  • March 2009
  • February 2009
  • January 2009
  • December 2008
  • October 2008
  • September 2008
  • July 2008
  • June 2008

Meta

  • Log in
  • Entries feed
  • Comments feed
  • WordPress.org
  • PAW Business
  • PAW Financial
  • PAW Healthcare
  • PAW Industry 4.0
  • PAW Climate
  • Data Driven Government
  • Deep Learning World
  • Machine Learning Times
  • Machine Learning course
  • Become A Sponsor
  • About
  • Blog
  • Previous Events
  • Call for Speakers
  • Press Room
  • Bestselling Book
  • Predictive Analytics Guide
  • Contact

Join us on:

Twitter
Facebook
LinkedIn

© 2023 Predictive Analytics World | Privacy
Produced by Prediction Impact, Inc. and Rising Media, Inc.