Risks in Machine Learning Projects and How to Avoid Them

Here’s a list of some of the most common risks in machine learning projects:

• Lack of understanding of your business goals

This results in excessive work needed to deliver higher and higher metrics, which may not solve the real problem.

• Lack of high-quality data

We tend to think we have the data we need to solve the problem; however, you often have to thoroughly investigate its quality and quantity beforehand.

• Lack of cross-team communication

A lack of communication between data scientists and backend or frontend development teams results in poor model deployment architecture or bad design choices.

• Lack of domain experts on the team

What you need to do first to avoid problems at a later stage of work is to define the business goal of the project. You should consider what purpose your project is meant to serve and what results you expect.

Is your goal feasible? Should you use the technology you already have in your tech stack, but with new data? Should you integrate your model with the solution you already have? You should be aware of the implications that a given approach will have for further work.

It’s also crucial to specify the end result that you expect as well as the budget. You should stay realistic and be aware that a limited budget (which is usually the case) may not bring you the results you’re after.

Proper research before you start your project may also prevent you from doing unnecessary work. Sometimes it turns out that you don’t actually need ML for your project.

It’s also possible that some of your ideas may prove too time-consuming, too costly, or too difficult to implement. More often than not, what you really need is a simpler technology; the sooner you realize it, the more time and energy you will save.

Rushing to use ML in your project is just one mistake you can make. Sometimes your expectations regarding ML may be too high.

“Some people seem to think that machine learning is some kind of a magic wand and all you need to do is to wave it and you’ll get the results you want,” says Krzysztof Sopyła. “This isn’t the case. ML will bring you the results you expect only if you do all the necessary work early: specify the goal of the project and ensure the quality and amount of data you’re going to use.”

Data is indeed the key to a successful ML project. As they say, “Garbage in, garbage out”—good data makes a good project, which is why collecting and preparing data should be the foundation of any product.

“It is said that 80% of the work is about preparing and cleaning data, while 20% is about modeling and preparing the algorithm,” Krzysztof states.

You need to prepare enough high-quality data from a given domain. Even if some company has a good algorithm for specific data, you can’t just transplant it directly into an ML project at a different company operating in a different domain.

Collecting data isn’t always easy. What you need to do at the very beginning is to take a look at the sources of data you already have. Providing a data sample is one of the crucial things to do at this stage. This will show you if you’re well-prepared for the project you want to develop.

“I learned the following trick when discussing ML projects with my potential clients: I ask them to send me 100 data samples by the end of the week,” says Krzysztof. “If they can’t do it for some reason, or if they have organizational problems collecting the data, that means they aren’t ready to start the project and some data engineering work should be done.”

In that case, you need to take a step back and see what you can do to obtain the data. You may use a public repository, but remember that accessing some of them requires permission, and not all of them are available in every country.

It’s a whole process: searching, checking who worked on what, contacting authors, and asking them to make the data available. It’s essential to determine whether your project is feasible at all since it’s impossible to make it work without access to high-quality data.

Not having enough viable data to work on is what will eventually make you start reorganizing your plans. People in charge of ML projects are often surprised to hear so many questions about their data. “Clients usually want to talk about specific solutions we can implement, or about algorithms that they tend to overrate at the early stage of project work,” says Krzysztof Sopyła.

Having access to data is crucial, but the way in which you collect it is important, too; when doing it, you need to take a lot of factors into account. When collecting the data, you need to think of how it was gathered so that the data isn’t biased.

You also need to think about whether the data is inclusive and representative enough. Sometimes even details play a huge role here, which means that you should consider as many variables as you can.

Collecting high-quality and unbiased data is what makes you see the big picture from the very beginning. This, however, can’t be done without the help of experts in particular fields. Their insight helps pinpoint deficiencies or inaccuracies that the team sometimes fails to notice.

Any model you produce and train should be made available to experts. Depending on the project domain, these can be, for example, linguists, ecommerce managers, or doctors. Prepare a small demo and encourage them to try it out and give you their feedback.

Thanks to this, you’ll be able to quickly spot any biases and wrong predictions, and correct the model or training dataset accordingly. Professional insight is essential, as you’re only able to understand certain things thanks to their expert knowledge and intuition.

Communication is the foundation of a successful ML project. The team and the experts involved may experience communication difficulties at first, but over time they should learn how to give feedback to each other and cooperate better. Don’t hesitate to communicate your ideas and doubts to the Product Owner, too. Ask them about their thoughts on the data, the model, and the results.

Being disciplined when working on an ML project is no less important than it is in other IT projects. To implement a model, you need to coordinate the work of a lot of people, including backend developers, frontend developers, ML team members creating algorithms, and data engineers.

You also need to know how to provide data to particular teams. Making sure that they communicate effectively is extremely important; the team that implements the model should understand ML and know what to ask about when developing your product.