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Father, Artist, Happy. Creator of MLOps community and Lover of AI Ethics

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 parallels with DevOps. If you’re coming from a software development background, it’s one of the easiest ways to get your head around why MLOps exists.

In DevOps, you’re bringing together the programming, testing, and operational elements of software development. …


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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 manage compared to standard OOP/FP Python

Stefan Krawczyk

Netflix perspectives:
https://netflixtechblog.com/notebook-innovation-591ee3221233
https://netflixtechblog.com/scheduling-notebooks-348e6c14cfd6

Sam Bean

I’ve heard of people maintaining notebooks with full unit test suites in production, to me it sounds like nonsense

Demetrios

ohhhhhh boy! this should be a good thread…

Stefan Krawczyk

papermill is great for creating “reports”…


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

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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 Luke over the weekend about the tool and why they created it. Before we talked about the tool though, first thing he said to me was “I saw your LinkedIn post….. yeah sorry about that” 😆

Anyway, I asked him why the hell he would make a monitoring solution at…


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 don’t know, MLOps World is a leading community of machine learning professionals based in Canada. Since 2017 they’ve been running an impressive North American local events program plus their annual big blast conference, which is happening this year on June 14th and 17th.

I’ve spoken at their events in the…


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 data scientists use to feed their machine learning models. To an outsider, creating a data pipeline might sound mundane or trivial, but it can involve weaving together anywhere from ten to thirty different data technologies.

The data engineer is also the person who selects the tools to do the job…


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‘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 of AI-related initiatives die on the vine. More go bad than should.

While there are lots of reasons why an ML project might not see the light of day, today I want to focus on one in particular: the tendency in some corners of data science to strive single-mindedly toward…


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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. But does it always make sense to automate everything in the machine learning lifecycle?

A lot of people think otherwise.

At first glance, you could assume that making ML deployments automatic would simplify processes, reduce errors and make deployments happen faster. …


You want proof!?!? here we go….

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Back in machine learning’s early days, every project was a trial run and many didn’t see daylight. Then big names with deep pockets like Google, Facebook and Amazon started pouring money into pioneering projects, and some of them have delivered a return on the investment — along with a lot of technical debt.

Despite the cloudy picture, there’s loads of anecdotal evidence that we’re entering the phase where we’ll look back and say ‘here’s when ML really arrived as a profession’.

With so many businesses launching their own machine learning initiatives, best practices are…


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Each interview we have done with guests has been unique and insightful. that being said since we are putting out so much content and there are so many new people joining the community every day I wanted to put together some of my favorite interviews as a ‘best of’ over the last year.

This was really hard to boil down to a list that didn't go into the double digits and if there is enough interest I can make a part 2. But for now, I have compiled in no particular order the following list.

D. Sculley
Elizebeth Chabot
Todd Underwood
Lak
Lina Weichbrodt
Feature…


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The pandemic has been brutal on collaboration. It doesn’t matter what secto you look at. Millions of people found themselves turned suddenly into remote workers when coronavirus landed. That included a lot of data scientists.

Of course, another shift was already underway. Machine learning teams were growing larger, and ML projects were getting more complex. Teams were becoming diverse and cross-functional, pulling together data modellers, visualisation experts, software engineers, product managers, designers, and so on.

That’s a sure sign that MLOPs is maturing, but it’s also made collaboration on machine learning projects more complex. It’s not that remote working was…

Demetrios Brinkmann

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