Category: Data
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Waste not Programmable
Programmable 2025 Melbourne featured a fantastic program, heavy on practical data & AI in an apt reflection of the times. It was also a great chance to catch up with many in the Melbourne tech community. I presented my 7 Wastes of Data Production talk. In the spirit of continuous improvement, I made some minor…
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22 rules of generative AI, 2 years on, part 1
How has my original post on 22 rules of generative AI aged in a period of rapid change? Are these solution considerations as enduring as I thought? Let’s reflect on the original advice and developments in the meantime. Apologies again for minimal references as I timeboxed the writing. In general, evidence should be discoverable/verifiable with…
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Charger hopping
While it’s nice to make good time, when in remote areas, sometimes any route will do. For EV road trip planning in trippler, I first found the direct route, then chose chargers along the route. This approach can fail when there aren’t enough chargers close to the route, as in remote areas. Charger hopping is…
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AI conversations for every role
We always envisaged Effective Machine Learning Teams (EMLT) speaking to multiple roles. Recently we’ve spoken to 4 distinct audiences about ML development, and you can listen in too. Whether you identify with: there’s a podcast* for you with popular presenters – see more below. Product We joined a Product Tank Melbourne event, along with Ana…
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Tech Lead Journal
The lastest in our series of podcasts on Effective Machine Learning Teams is now live on the excellent Tech Lead Journal by Henry Suryawirawan. Check out the episode titled Building Effective and Thriving ML Teams. It’s been fantastic to talk with many great names in technology recently about EMLT, and I think the variety of…
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Stop thinking
EV road trips have a different cadence. You might need to charge an EV up to twice as frequently as you’d refuel an ICE vehicle (depending on the pair compared). However, given people need to stop too, this doesn’t necessarily mean trips take longer, and the different pattern of stops may even make the trip…
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Joe Reis show
I had a great chat with Joe Reis on The Joe Reis Show, along with one co-author of Effective Machine Learning Teams, David Tan. We covered the motivation for the book, scenarios where we’d helped teams address issues to become more effective, and the tools we’d developed from those solutions. We also talked about a…
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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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Effective topologies for Data and ML teams
I presented this talk at the Melbourne Data Engineering meetup, on a wild and wet Friday night. Having your cake and eating it too – Effective topologies for Data and ML teams (slides) In the talk I explore how Team Topologies provides patterns for reconciling fast flow of value with (multiple) specialisations in data and…
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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…