Category: Machine Learning
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Effective ML Teams on Thoughtworks Tech Podcast
I recently recorded an episode of the Thoughtworks Technology Podcast with my Effective Machine Learning Teams co-authors Ada Leung and David Tan, hosted by Scott Shaw and Ken Mugrage. The episode is number 146 – Building at the intersection of machine learning and software engineering. It was great to chat about the book and share…
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EMLT Q&A
A fun Q&A with Thoughtworks on the drivers, key messages and writing process for Effective Machine Learning Teams (EMLT) with my fellow authors Ada and David. It’s neat to be featured alongside all the other many great books from Thoughtworks authors. Find the book, trial and purchase options at O’Reilly, and find yourself a nice…
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Dealing with data inventory
Data held by businesses is often described as an asset. This can be misleading or even incorrect. In either case, data managed inappropriately leaves value on the table, inflates cost, reduces responsiveness, and creates risk. Some data held by businesses would better be described as inventory. It might one day be a true asset, but…
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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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Effective Machine Learning Teams
I’m very excited to be writing a book with my colleagues David Tan and Ada Leung. The topic and title Effective Machine Learning Teams was born from our combined work on team technical and delivery practices, and wider organisational patterns, applied to developing machine learning applications. The book has two landing pages where you can…
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Getting serious about testing AI
I was delighted to contribute to a Thoughtworks’ insights article on AI testing in response to Forrester’s recent report It’s Time To Get Really Serious About Testing Your AI. The report rightly raises the importance of testing in AI systems and highlighted Thoughtworks’ Continuous Delivery for Machine Learning (CD4ML) approach. The response also discusses other…
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A gentle introduction to embeddings at the inaugural GenAI Nework Melbourne meetup
I was thrilled to help kick-off the GenAI Network Melbourne meetup at their first meeting recently. I presented a talk titled Semantic hide and seek – a gentle introduction to embeddings, based on my experiments with Semantle, other representation learning, and some discussion of what it means to use Generative AI in developing new products…
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Perspectives edition #27
I was thrilled to contribute to Thoughtworks Perspectives edition #27: Power squared: How human capabilities will supercharge AI’s business impact. There are a lot of great quotes from my colleagues Barton Friedland and Ossi Syd in the article, and here’s one from me: The ability to build or consume solutions isn’t necessarily going to be…
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A coding saga with Bard
Though but a footnote in the epic of coding with AI, I though it worth musing on my recent experience with Bard. Bard currently uses the LaMDA model, which is capable of generating code, but not optimised for it. The story might be different with Codey as protagonist (or is that antagonist?) I didn’t produce…
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Smarter Semantle Solvers
A little smarter, anyway. I didn’t expect to pick this up again, but when I occasionally run the first generation solvers online, I’m often equal parts amused and frustrated by rare words thrown up that delay the solution – from amethystine to zigging. The solvers used the first idea that worked; can we make some…