Back in 2012, US retail chain Target created an algorithm that could work out if customers were pregnant based on their past purchasing behaviour. After analysing historical data, they used the results for a direct marketing campaign, snail-mailing coupons for baby products to the home addresses of likely new-mums-to-be. It…

This is part one of a two-part series where I try to synthesis some of my understandings of the parallels between DevOps and MLOps. When I first got involved in MLOps, there was a steep learning curve. One of the things that helped me climb it was to look at…

The easiest way to get a good thread going on the community slack is to ask about notebooks. Here are a few of my favorite threads on Jupyter notebooks and the love/hate relationship we have of them in the MLOps community. Should you put notebooks in production? Sam Bean I find them more challenging to change…

A collection of all the latest New Tool Tuesday excerpts from our MLOps weekly newsletter. Subscribe to get them fresh in your inbox here. Kites and Boxes Another ML Monitoring Solution? There is something very special about today’s “New Tool Tuesday”. My old boss and an MLOps community founder Luke Marsden is a co-creator. I spoke with…

Super-excited to announce that we’re joining forces with our sister organization across the pond, MLOps World. Together we’re going to co-promote the upcoming MLOps World Conference and bring our own MLOps Community ideas and content to an even bigger global audience of data scientists and ML pros. For those who…

With the rush to embrace machine learning, data engineering has turned out to be an essential piece of the ML puzzle — one that data scientists need to rely on more and more as a pre-requisite for success. With their expertise in programming, data engineers create the data pipelines that…

‘If you’re not embarrassed by your first product release, you’ve released it too late’ — Reid Hoffman We’ve all heard of the study that says more ML projects fail than succeed. Even if you don’t accept the figure that’s usually bandied about, it’s reasonable to say that a fair number…

We’ve all seen Google’s now-infamous paper (here, ICYMI) where they make a sweeping case for automating ML pipelines — pretty much from end to end. We’ve had a fair amount of pushback from the community on that. There’s lots in the DevOps domain that MLOps teams can learn from. …

You want proof!?!? here we go…. Back in machine learning’s early days, every project was a trial run and many didn’t see daylight. …