I’m going to use a bit of a click bait title for this article. But the aim of this article is to share experiences I’ve gathered from about 10 years building ML systems, and building ML teams. Why ML is about more than ML I saw the following tweets by Erik so I’ve added them.… Continue reading Why building ML systems is about more than ML?
My company http://www.aflorithmic.ai recently went through some migrations. There were a few from the easy – moving from python anywhere to AWS Elastic Beanstalk, to the more involved – a big refactoring that involved moving ejecting from Expo. However, I think the lessons apply. Firstly, I’m not an expert. I’ve been involved in migrations… Continue reading Migrations – or how to fix technical debt
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Disclosure: I’m not really a data scientist these days, I’m a founder of a tech startup (which has a core AI component). These are my unfair, biased, and prejudiced views based on experience of being a professional data scientist for something like a decade. So firstly, I think it’s worth declaring that machine learning and… Continue reading How can Data Scientists survive layoffs?
What is synthetic media? Synthetic Media includes artificially-generated video, voice, images or text, where AI takes on part (or all) of the creative process. This falls under the broader landscape of synthetic, artificial or virtual reality (photo-realistic AR/VR). One of the more powerful things about modern machine learning or AI methods is that they can… Continue reading Synthetic Media – What’s coming next?
Unbundling of people or ‘rise of the creator class’ If you work in Tech you end up exposed to trends, and if you’re a product-focused engineer you think a lot about ‘product’, product strategy and what’s ‘coming next’. One question in our current ecosystem is ‘what’s next after marketplaces’ we’ve already seen very successful companies… Continue reading The rise of the creator class
One of the distinctions I like to make about modern ‘Data Science’ is between ‘data science for decision support’ and ‘machine learning’. h Basically speaking machine learning, which is often product-focused – is generally something like ‘there’s this problem in fraud, credit scoring’ and we need an automated and deployed system. You’ll often work super… Continue reading Data Science for Decision Support: Or why Bayesian Analysis matters
I’ve been working on software products for several years now. And while a lot of my work has been greenfield, I’ve struggled to articulate the different kinds of work that happens in product development. Des Traynor one of the founders of Intercom had a great talk about this. (Source: Video) Often people don’t understand the two… Continue reading The two kinds of work in Software Products
Some lessons learned from React Native Development I’ve recently been writing some react native so I wanted to enumerate an opinionated list of things I’ve learned. I’d not done any mobile development before this, so consider this a reasonable approximation of how an experienced engineer would get their heads around that ecosystem. Use SDK/ Managed Services… Continue reading 4 Lessons learned from React Native development
I recently wrote an email to an Irish Entrepreneur about the challenges of using Kubernetes and Kafka. I think both technologies are awesome, but I’ve seen in the past companies trying to integrate them without thinking about the managed services that exist out there. Kubernetes If you’re doing it yourself I’d look at https://docs.aws.amazon.com/eks/latest/userguide/getting-started.html which is from… Continue reading Why serverless matters or how do you want to spend your innovation tokens?