How not to fail at machine learning before you even begin

Publisert 21.03.2021

Artificial intelligence is transforming the world and creating enormous value for those that use it well. It’s exciting. But for most people AI is a bit mysterious, and they don’t know what it looks like in practice or how to start using it themselves. Data, expertise and enthusiasm are crucial for the success of a machine learning project, but without proper scoping and planning you will fail before you even begin.

How to scope and plan machine learning projects

Are you itching to bring AI into your own domain but don’t know where to start? Or have you already started on a machine learning project but feel lost and overwhelmed?

One of the most important tasks we do as machine learning consultants is to help our customers scope and plan their machine learning projects.

Many of our customers, even if they’ve already collected the data they need, don’t know where to start. And even the most experienced data scientist can spend months lost down rabbit holes or perfecting a model that solves the wrong problem.

This is why scoping and planning is so important. Being clear on your goal and how you will reach it right from the outset is crucial to the success of your project.

Do you know the most common pitfalls?

For every success story in machine learning there are tens of projects that fizzle out, largely because they were not well-planned. Without proper planning, you could waste valuable time, resources and money. Some of the most common pitfalls include:

– A goal that is not well enough defined

– No clear plan for how to use the final result

– Incorrect allocation of resources

– Insufficient data quality checks

– Under-estimating the importance of labels

– Lack of understanding of the limitations of different approaches

– Overly-optimistic time estimates

– Failure to account for the iterative nature of machine learning

– No plan for how to evaluate the success of the project

Wanna know how not to fail at machine learning before you even begin?

So how do you avoid these problems? Where do you start? How do you plan a machine learning project? How do you estimate how long it will take? What resources do you need? What does a machine learning project look like in practice? Why should you even bother? Join us for a webinar where we will talk about scoping and planning machine learning projects. We’ll cover the entire project life cycle and give you our best tips for making your project a success.

Do you want to learn more about what it is, and how not to fail in your attempt to create new value with machine learning.
Watch our recording of the webinar or contact us for more information!

C. Sundts gate 17-19
Jonny Klemetsen
Universitetsgata 2
Haakon Skramstad
Olav Tryggvasons gt. 40
Nadeem Qureshi