For the past two weeks I have, like most people, been working from home. Doing fun stuff just for yourself during this time can be incredibly important, and with this in mind I’ve started going through some of the Lego and other building kits I’ve got and just not had time to open. The first of these that I’ve tackled is this years Ideas (fan-designed) Lego set the international space station.
One of the things that I have been complaining about with many of the data science masters courses is that they are missing a lot of the basic skills that are essential for you to be able to be effective in a business situation. It’s one of the things I was going to talk about at the Women in AI event that was postponed this week and I’m more than happy to work with universities who want to help build a course1. That said, some universities are realising this is missing and adding it as optional courses.
Continue reading Data Science Courses – the missing skills you need
If you’ve been to any of my technical talks over the past year or so then you’ll know I’m a huge advocate for running AI models as api services within docker containers and using services like cloud formation to give scalability. One of the issues with this is that when you get problems in production they can be difficult to trace. Methodical diagnostics of code rather than data is a skill that is not that common in the AI community and something that comes with experience. Here’s a breakdown of one of these types of problems, the diagnostics to find the cause and the eventual fix, all of which you’re going to need to know if you want to use these types of services.
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