Category: Products
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An evergreen question: what is an MVP?
I was asked this the other day. My answer was: it depends on the context; it helps to have an example. And the contextual element is probably why this remains an evergreen question. While I have a fresh example in mind I thought I’d quickly plant a stake in the ground for reference. First, it…
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Ur Uber
The other day a former classmate described a project of mine in 2010 as “the idea for Uber before Uber was even a thing”. Nearly 15 years later, I thought it would be interesting to pen some reflections on how we came by the idea and why we didn’t pursue it. Mowbb Mowbb* would make…
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trippler development notes
This is the behind-the-scenes companion post to a resilient charging planner, sharing more on the development process and how the app works, with links back to earlier work. Design philosophy I wanted to focus on the core problem of interactively exploring charging options under varying trip requirements. I wanted a tool that I would use…
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A resilient charging planner
Check out the prototype of trippler, an interactive charging planner for resilient EV road trips. Based on my own EV road trip experience, trippler is as much about easily understanding charging options and contingencies, to reduce charger anxiety, as it is about coming up with a single best plan. If you’re looking for the latest…
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GenAI stone soup
GenAI (typically as an LLM) is pretty amazing, and you can use it to help with tasks or rapidly build all kinds of things that previously weren’t feasible. Things that work some of the time. The soup But do you find yourself reworking large chunks of generated content, or face major hurdles in getting a…
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Hopsworks and multidisciplinary ML
I recently had a brief but fun chat with Hopsworks about the multidisciplinary nature of building machine learning products, as part of their 5-minute podcast series hosted by Rik Van Bruggen. See the transcript and video at 5-minute-interview-with-david-colls-nextdata. Rik and I talked about how David, Ada and I address this multidisciplinary perspective in our book…
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Demistifying ML product teams
Along with Ana Kelk, Head of Product (ecosystem) at Canva, David Tan, Ada Leung and I participated in a panel for Product Tank Melbourne, discussing the particular needs of ML product development (and the common product development needs too!) It was a great way to share and get feedback on one of the key themes…
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Effective Machine Learning Teams in print
My book Effective Machine Learning Teams is now in print! Building ML solutions requires multi-disciplinary collaboration. EMLT shows how to use design practices to identify the right products, how to apply good data science and software engineering practices to build products right, and how to structure ML teams and organisations so that they are right…
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Humour me – DRY vs WRY
Don’t Repeat Yourself (DRY) is a tenet of software engineering, but – humour me – let’s consider some reasons Why to Repeat Yourself (WRY). LEGO reuse lessons In 2021, I wrote a series of posts analysing LEGO® data about parts appearing in sets to understand what it might tell us about reuse of software components…
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22 rules of generative AI
Thinking about adopting, incorporating or building generative AI products? Here are some things to think about, depending on your role or roles. I assume you’re bringing your own product idea(s) based on an understanding of an opportunity or problems for customers. These rules therefore focus on the solution space. Solutions with generative AI typically involve…