Working backward from winning & preparing for future tech innovation w/ Evan Welbourne #181
The Engineering Leadership Podcast
Exploring the Machine Learning Maturity Framework and Stage Gates for Product Development
Exploring the significance of stage gates within the machine learning maturity framework for AI product development and the critical stages involved, from idea generation to product maturity. The chapter also addresses challenges in the basic stage, the role of feedback loops, and the impact of product management.
We discuss how to navigate the delicate balance between meeting current customer needs while also preparing for future tech trends & opportunities with Evan Welbourne, Head of AI and Data @ Samsara. Evan dissects the rapidly transforming pace of developing AI/ML products, sharing strategies for merging conversations around differing product-building processes, tips for moving seamlessly / gaining approval between product development stages, defining what customer success looks like, methods for working backward from problems, and best practices for avoiding friction throughout the product development process. He also shares frameworks for envisioning & working toward future tech possibilities while simultaneously developing hypotheses that inform future direction, creating diverse AI/ML team composition, and effectively communicating with stakeholders.
ABOUT EVAN WELBOURNE
Evan Welbourne is the Head of AI and Data and Samsara, leading the organization’s machine learning, computer vision, data science, and data analytics teams – as well as data engineering and data platform for the company. He has a long-standing career in both machine learning and IoT. Before Samsara, Evan held various roles at Amazon, including the Head of Machine Learning for Alexa Smart Home and Manager of the Computer Vision Research Group. He also led research teams at Samsung and Nokia.
Evan earned his Ph.D and M.S. in Computer Science and Engineering from the University of Washington and holds a B.S. in Computer Science and Mathematics from the University of Toronto.
"With AI, there's something new every week. You could stay in those stages forever. You can just keep iterating and trying new things, but at some point you have to kind of cut it off. You've got to time box it and just go with something that you know will work. You're constantly also calibrating between the quality of what you're delivering and the time it takes you to deliver it. A lot of that problem backs into this early stage of the process. We do want to do a good job of understanding opportunity but there's analysis paralysis. We don't want to just get stuck there.”
- Evan Welbourne
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SHOW NOTES:
- Staying customer-focused while working toward the future @ Samsara (3:22)
- Merging forward-looking technology & customer-problem-focused product-building conversations (5:54)
- Defining customer success & working backwards from winning (8:38)
- How stage gates can confirm / assess feature accuracy & maturity (10:58)
- What the approval moment looks like while moving from stage to stage (15:29)
- Understanding what stages offer the greatest opportunity for risk / friction (17:11)
- Signals to watch for that allow you to move forward with confidence (19:30)
- Best practices for anticipating & preparing for future possibilities (21:13)
- Using smaller-scale projects to inform future direction of larger-scale products (23:12)
- Communication strategies for working with less technical stakeholders (25:22)
- Methods for effectively communicating complex, technical information (27:59)
- AI / ML team composition at Samsara (30:04)
- Frameworks for aligning & motivating folks to focus on customer needs (32:59)
- Strategies for introducing new technologies & scientific research into your teams (35:06)
- Introducing AI into mission-critical internal tools (36:34)
- Rapid fire questions (39:17)
LINKS AND RESOURCES
- Human Compatible: Artificial Intelligence and the Problem of Control - Stuart Russell lays out a new approach to AI that will enable us to coexist successfully with increasingly intelligent machines.
This episode wouldn’t have been possible without the help of our incredible production team:
Patrick Gallagher - Producer & Co-Host
Jerry Li - Co-Host
Noah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/
Dan Overheim - Audio Engineer, Dan’s also an avid 3D printer - https://www.bnd3d.com/
Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/
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