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.
