Key mistakes to avoid when training your AI model Blog • Jun 6, 2020 • 3 min read

When training your AI model, it is crucial to use a wide set of data and rigorous testing to ensure that your model functions reliably. As technologically advanced machine learning AIs have become over the years, there have also been countless incidents where an improperly trained AI model caused some hilarious yet disastrous results. Here are a few common mistakes to keep in mind when training your model in order to prevent your AI from going off the rails.

Mistake #1 - Using unverified data

Machine learning AIs need tons of data in order to be properly trained. That being said, one of the most common mistakes that developers make is using unverified data that could contain dirty data points. “Dirty data” includes simple mistakes, missing variables, conflicting data, errors, poorly categorized data, and any other data that could cause unnecessary anomalies during the training process. Carefully examining your raw data set and doing your due diligence eliminating dirty data can greatly improve the reliability of your AI model.

Mistake #2 - Re-using the training data to test your model

Let’s say you gave a student a set of questions and answers to study, only to test this student later with the exact same set of questions. The student will easily ace the test, but not by any means does this imply that the student mastered the subject in any way - they just knew all the answers to begin with. The same logic can be applied to machine learning AIs. The AI learns by going through mountains of data points in order to accurately predict the solution to any given problem. When testing the capabilities of your AI, it is essential to test using brand new data points that were not part of the machine learning process.

Mistake #3 - Failing to check if your AI is biased

No AI model can be flawless and perfect. As humans don’t operate on a unified algorithm like robots, there is bound to be human biases in your gathered data. Many sociocultural factors such as age, gender, orientation, income level, and ethnicity can affect the response one way or another. One of the most effective ways to minimize this is by using statistical analysis to identify how each personal factor affects your data and factoring it into the AI’s calculations for improved accuracy.

Mistake #4 - Completely relying on your AI to learn independently

This may sound like contradicting advice since the whole point of a machine learning AI is for it to independently repeat the learning process by going through massive amounts of data. However, it is extremely important to do your part as a developer in making sure that the AI is consistently on the right track. By frequently spot-checking the AI throughout the process and comparing intermittent results to real-life outcomes, you can easily prevent your AI from straying far away from reality.

When developing a machine learning AI, keep asking yourself important questions. Is my data sourced from a trustworthy source? Am I making any assumptions that could distort the results? Is my AI applicable to a wider demographic?

Here at Inresource , we strive to help your machine learning models get the best training data. As the old saying goes, a student is only as good as his teacher. Get up to 50 hours of free demo , today.