5 Common Machine Learning Mistakes and How to Avoid Them

Fortunately, there are a lot of technologies that can help in data collection, processing and analysis. This is where machine learning enters the picture.

As the name suggests, machine learning is a technology that uses computer algorithms that automatically and gradually improve using data. It survives and thrives on data.

That said, it’s still not perfect. Just like other analytics methods and technologies, it’s still prone to encounter issues from time to time.

In this article, we are going to share with you the most common machine learning mistakes – and how you can avoid them.

Mistake #1: Overlooking the need for competent in-house data scientists

We can’t blame you. Given the demand and shortage of deep analytics talent, there’s no wonder why looking for competent data scientists to hire can be quite a challenge.

In addition, many data scientists can be pretty selective with their choice of projects and companies to work with. They also tend to require high compensation, especially when compared to their peers from other areas of expertise.

For these reasons, a lot of companies simply resort to hiring freelance data scientists based on need. Unfortunately, you can’t ensure that your machine learning program will run this way smoothly.

Instead, we recommend the following solutions. First, we believe that the best way to hire affordable yet competent data scientists is to forge a partnership with universities, particularly those with strong data science degrees.

You can form an internship program that will provide value to you, the students and the academic institution.

Another solution is to invest in quality data science training. In this way, you’ll be able to source your talents in-house and therefore, be able to negotiate better rates and goal-oriented projects.

Mistake #2: Collecting poor quality data

Data collection is more than just spending time to accumulate volumes of data. After all, not all sources and bits of information are the same. Some data issues include misleading information, inconsistent values, and biased data.

For instance, suppose we are dealing with medical information and looking at the use of nanotechnology in healthcare. Being a technology that not many people understand, it’s understandable why there is a lot of controversy surrounding it.

As such, there are also a lot of data sources that may produce ill-advised and biased information.

Fortunately, there are steps that we can take to mitigate such problems. The first step is to strategically plan where the data is going to be collected.

Strengthening data security and governance should be prioritized as well.

Finally, the data scientists themselves should be able to minimize these issues through proper data preparation. In this way, they’ll be able to catch inconsistencies earlier.

Mistake #3: Implementing machine learning without a solid strategy

Are you simply implementing this technology just to keep your company up to date with current trends? Do you really understand what it takes to have a proper machine learning model?

For instance, there are a lot of companies that have fallen prey to machine learning implementation even if they don’t really have the proper infrastructures for it. Storage, computation, talent; there are a lot of factors that should be carefully considered before committing to machine learning.

You can minimize the issues that this lack of resources might cause with a sound strategy formed with the advice of data experts.

Mistake #4: Not fully understanding your machine learning model

Speaking of strategizing with data experts, the next mistake that many companies make (even the biggest corporations) is not fully understanding their machine learning model.

Taking the time to at least get an idea of how it works will allow you to better incorporate your machine learning model into your company’s next plans and goals, and thus, lead to better utilization.

Mistake #5: Failure to recognize failure

Finally, one of the biggest mistakes companies make when implementing machine learning is not performing proper failure analysis. It’s crucial to determine the top problem categories, what the particular issues are, how often they occur and more importantly how to resolve them.

In summary

Machine learning is becoming more and more essential in running a business in the digital age. However, as with other technologies, it’s possible for users to stumble upon issues every once in a while.

Fortunately, most of these problems can be resolved by hiring the right people, gaining a better understanding of how machine learning works and recognizing potential issues early on.