What Are The Major Machine Learning Challenges

Machine Learning

A career in machine learning can bring job satisfaction, great growth, and high pay, but it’s a complex and challenging process. While many researchers and experts agree that we are in the golden age of artificial intelligence, there are still many hurdles and challenges to overcome when developing projects. With all of this in mind, let’s take a look at some of the hurdles companies face in developing machine learning technology.

7 Major Machine Learning Challenges

Machine Learning Challenges

Lack of Training Data

In the process of machine learning, the most important task is to train data to obtain an accurate output. Less training data will yield inaccurate or overly biased predictions. Let us understand this with an example. Consider a machine learning algorithm similar to training a child. One day, you decide to explain to a child how to tell the difference between an apple and a watermelon. You’ll take an apple and a watermelon and tell him the difference based on color, shape, and flavor. In this way, he will soon be perfect in distinguishing the two. But on the other hand, machine learning algorithms require a lot of data to differentiate. For complex problems, training on millions of data may even be required. Therefore, we need to make sure that the machine learning algorithm uses enough data for training.

Overfitting the Training Data

Overfitting refers to a machine learning model trained with a large amount of data that negatively affect its performance. It’s like trying on oversized jeans. Unfortunately, this is one of the important issues facing machine learning professionals. This means that the algorithm is trained with noisy and biased data, which will affect its overall performance. Let us understand this with an example. Let’s consider a model trained to distinguish between cats, rabbits, dogs, and tigers. The training data includes 1000 cats, 1000 dogs, 1000 tigers, and 4000 rabbits. Well, there’s a good chance it will identify the cat as a rabbit. In this example, we have a large amount of data, but this is biased; therefore, the predictions are negatively affected.

We can solve this problem by:

  • Analyze data at the perfect level
  • Use data augmentation techniques
  • Remove outliers in the training set
  • Choose a model with fewer features
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Machine Learning Is A Complex Process

The machine learning industry is young and constantly changing. Rapid strike and test firing experiments are underway. The process is changing, so the potential for error is high, which complicates learning. It includes analyzing data, debiasing data, training data, applying complex mathematical calculations, and more. So, this is a very complex process and another huge challenge for machine learning professionals.

Insufficient Fitting of Training Data

This process occurs when data cannot establish precise relationships between input and output variables. It just means trying on small jeans. This means that the data is too simple to establish precise relationships. To resolve this issue, do the following:

  • Maximize training time
  • Increase model complexity
  • Add more functionality to your data
  • Reduce general parameters
  • Increase the training time of the model

Execution is Slow

This is one of the common problems faced by machine learning professionals. Machine learning models are efficient in providing accurate results, but it takes a lot of time. Slow programs, data overload, and excessive demands often take a lot of time to provide accurate results. Furthermore, it requires constant monitoring and maintenance to provide optimal output.

Algorithms Are Flawed When Data Grows

So you find high-quality data, train it surprisingly well, and make predictions that are clean and accurate. Yay, you’ve learned how to create machine learning algorithms! ! But wait, there is a tipping point; as the data grows, the model may become useless in the future. The current best model may become inaccurate in the future, requiring further rearrangement. Therefore, you need regular monitoring and maintenance to keep the algorithm working properly. This is one of the most exhausting problems facing machine learning professionals.

Poor Data Quality

Data plays an important role in the machine learning process. A significant problem facing machine learning professionals is the lack of high-quality data. Dirty and noisy data can make the whole process extremely exhausting. We don’t want our algorithms to make inaccurate or wrong predictions. Therefore, data quality is critical to improve output. Therefore, we need to ensure that the data preprocessing process (including removing outliers, filtering missing values, and removing unwanted features) is done with maximum perfection.


If the training set is too small, or if the data is not generalized, noisy, and corrupted by irrelevant features, the system will not perform well. We went through some of the basic challenges that beginners face when practicing machine learning.

Tina Jones