machine-learning
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Analytics, machine learning and Generative AI as the 3 core ways to get value from AI and data
Updated 5th March 2025 Do you keep hearing that “generative AI” and “machine learning” are changing the world, but don’t know where to start when figuring out how to implement them in your organisation? This short guide is for you. It’s intentionally non-technical, and slightly opinionated, but the goal is to give organisations a useful Continue reading
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Where do data scientists fit in with the animal advocacy movement?
Updated 16th Jan 2026 Perhaps about once a month someone messages me on LinkedIn or the Hive Slack community, asking how a data scientist can use their career to help animals. This is something I’ve spent nearly 4 years thinking about. I originally trained as a data science after my PhD because I wanted to Continue reading
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Three reasons you shouldn’t become a full stack data scientist (and 3 reasons you should)
Full stack. It’s a term of prestige in software professions, from devs to data scientists. Full-stack data scientists are involved in all aspects of the data science life cycle. Because they are involved in every stage of the process and design of what they are building, full stack data scientists own a project and deliver Continue reading
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I gave a talk at Manchester’s biggest AI event
A while ago I gave a talk at AltitudeX, Manchester’s biggest AI event hosted by Peak, the company I work for. Alongside a few of my colleagues, we gave a talk on how to communicate with non-technical stakeholders. Unfortunately, I can’t embed the talk, so you’ll have to follow the link. https://altitudex.live/watch-on-demand/?wchannelid=rs8or2iids&wmediaid=aj0yu8op81 Continue reading
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How much data do you need for a recommender model?
Do you want to build a recommender system like Amazon or Netflix, but aren’t sure if you have enough data? You’re not alone! It’s surprisingly hard to find good benchmarks and “rules of thumb” for how much data you need to build a good recommender. What makes it even harder is the fact that collaborative Continue reading
Thomas Manandhar-Richardson, PhD 