Stop delaying that deploy, take risks to base decisions on production metrics

This is story #3 of the series Flight checks for any (big) machine learning project.

Ok, at this point i suppose you already have clear KPIs and the right team.

The next typical mistake in machine learning projects that we should avoid is taking longer than necessary to get something in production.

And having something in production and having a machine learning model in production are two different things.


Building The team
Building The team

This is story #2 of the series Flight checks for any (big) machine learning project.

Great! you have clear KPIs and a more robust understanding of the problem.

As in any successful project, it is important to ensure that the team that carries it out has the necessary skillset.

In the case of machine learning projects, the required profiles are so novel that they can become somewhat confusing. Even IT professionals may not fully understand the differences between a data scientist, a business analyst, a machine learning engineer, a data engineer, a backend engineer, etc, etc.

Regardless of labels (which…


This is a story (actually it’s the first one) of the series Flight checks for any (big) machine learning project.

Nothing is more tempting when starting a machine learning project than, well … doing machine learning! After all, that’s what we’re here for, right? Let’s not wait any longer then and get our DNNs started, the spark cluster set up and … off to create value.

Hold your horses! In the real world, you don’t work that way.

The most common — and costly — mistake I’ve seen is starting an ML project without having clear KPIs.

To be fair…


Preparing your project
Preparing your project

So you are about to start a new machine learning project…

As a data scientist, machine learning engineer or sponsor of the initiative, there is nothing like boarding a new Andrew Ng Airline flight, taking a seat, relaxing and enjoying the flight. After a quiet journey propelled by the new magical electricity of artificial intelligence and led by autopilot algorithms, we will arrive at our destination “Optimized-metrics-land”. We just have to enjoy those competitive advantage cocktails we have earned.

Well, really … being down to earth, we know that if you are in the early stages of a project, in this analogy you will most likely sit in the cabin along…

Nicolas Rodriguez Presta

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