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April 08, 2024

What Are the Top 10 Challenges of Machine Learning?


Edwin Lisowski

CSO & Co-Founder

Reading time:

13 minutes

On our blog, we talk about machine learning every week. That’s no surprise. After all, machine learning consulting is one of our core services. We always try to present this area of AI in the most straightforward and transparent way. We believe that this approach is effective, especially when it comes to less tech-savvy readers.

At some point, you might even think that machine learning models aren’t that complicated at all. Unfortunately, that’s not true. Machine learning is an extremely complex field of data science, and there are still some significant challenges we still have to overcome in the future. And that’s what we want to talk about today. We are going to show you main challenges in machine learning.

Let’s cut right to the chase!

The challenges in machine learning that we’re going to talk about are of diverse nature. Some of them are related to data that’s always a base for every machine learning model. Some of them are related to aspects surrounding machine learning, such as cybersecurity. And finally, some of them refer to applications that we still need to perfect. So, without further ado, let’s take a look at our list!

Data science-related challenges in machine learning

First off, let’s talk about data science. Every machine learning model needs training data to “learn” how to work. And this very first stage entails some crucial challenges:

Challenge #1: Lack of training data

In general, machine learning models need training data–information and examples representing exactly what you want them to do for your company. Let’s use a straightforward example. You want to devise a new machine learning-based algorithm that will distinguish safe emails and spam.

In this example, emails are our training data. You need examples of safe emails, and you need instances of spam. The more, the better.


Sometimes, in order to train an ML-based algorithm, you’ll need hundreds of examples. And that’s good news because more often than not, you’ll need… Millions of examples. Yes, to train just one “simple” machine learning algorithm!

And the truth is, in many instances, you simply don’t have access to millions of real examples representing the exact same thing. That’s the very first challenge machine learning specialists have to overcome.

Challenge #2: Poor quality of data

In other instances, the data you need is available, but the quality of it leaves a lot to be desired. If you start work with poor-quality data, you can’t expect to end up with a fully functional and effective algorithm. On the contrary, it will be defective and inefficient. That’s why it is said that the vast majority of the data scientists’ work revolves around organizing and cleaning data. Because otherwise, it’s useless from the AI perspective, no matter how voluminous it may be.

And this is where data quality tools come into play. They are designed to remove formatting errors, typos, redundancies, missing entries, and other issues that reduce the quality of your data. And you can believe us, in the majority of companies, these errors are very common. In the machine learning world, the more doesn’t necessarily mean the better.

Challenge #3: Data overfitting

In short, data overfitting is all about developing a too complicated machine learning model and trying to fit it into a limited set of data. In the human world, it’s called overgeneralization.

Again, let’s use an example. Suppose you were recently robbed by a man wearing a black beanie. Will you jump to the conclusion that every person wearing a black beanie is a mugger? If you do, you will fall into a trap called overgeneralization. And that’s exactly what can happen in the machine learning world, too.

As a result, your machine learning model works brilliantly on a training dataset (yes, in that particular situation, black beanie = a mugger), but in more instances and cases, it fails to generalize properly (in the real world, black beanie doesn’t necessarily mean a mugger). That’s data overfitting.

Challenge #4: Dat underfitting

Here, we deal with a reverse issue. Your model is too simple or misses parameters that it should have included in order to produce a clear and unbiased result. This means that your machine learning model cannot draw useful conclusions from the training data.

Actually, the only way to deal with both these problems is to develop an algorithm that’s built strictly with a specific purpose in mind. There is no copy-paste here. Everything has to be adjusted and tailored to your assignment or project.

Challenge #5: Irrelevant features

When it comes to machine learning algorithms, training data is not everything. You also need a good set of features on which your algorithm can be trained. Let’s go back to our spam/non-spam emails example. There is a specific list of features describing every email: Content, subject line, used words, links, etc. All these features are relevant. But there can also be irrelevant features like sending time or kilobytes.

What does the fact that this specific email weighs 20kB and was sent at 11 pm tell you about whether it’s spam or not? Nothing! It’s an irrelevant feature that should be eliminated from your model. The more irrelevant features you have, the less helpful your final product will be.

In our example, deciding which features are irrelevant was intuitive and simple. In many real-life cases, you’ll have to think about this issue a bit longer.

Case Study-MLOps

Challenges related to machine learning projects

Here, we need to tackle three specific Machine Learning problems:

Challenge #6: Data security

Although we listed data security as one challenge, it’s actually multifaceted. For starters, you need to make sure that every framework, every third-party app, and every piece of your IT infrastructure is properly secured against diverse cyber threats. Secondly, bear in mind that your employees and coworkers can also be a source of the problem.

For example, the bring your own device policy (BYOD) can be very convenient from your employees’ perspective, but it can also be very risky. After all, how can you be sure that their private devices are properly secured? In most instances, you can’t.

Machine Learning challenge - data security

Another data security-related problem is fake data. This problem happens when your company is being attacked by hackers who replace your real data with fake information. Suppose you run a manufacturing company and you’re under a fake data attack. What could have happened if your real measurements had been replaced with fake ones? For instance, your devices could get a false temperature report, resulting in a severe malfunction.

The last element we want to talk about is related to access control. Here, the best way to avoid unnecessary complications lies in designing encrypted authentication and validation procedures so that users are verified before they can implement any changes into the system or data it stores.

Whatever you do, remember: As Clive Humby, a British data science entrepreneur, once said, “Data is the new oil”. It’s as valuable and fragile. Make sure you take good care of it and protect it from possible threats.

Challenge #7: Accessibility

Granted, in a way, you can have access to machine learning features for a small amount of money. Many SaaS platforms have built-in ML features, and they are not that expensive to buy. However, if you’re looking for a tailor-made machine learning (let alone deep learning!) algorithm that’s fully adjusted to the needs of your company, you have to prepare for some serious financial investment.

Of course, we always say that it’s a profitable solution in the long run because, with machine learning, you will be able to save a lot of time and manual work. But the upfront investment is significant, and many smaller organizations simply cannot afford to implement machine learning models, even if they wanted to. That’s one of the most pressing challenges that still wait to be solved.

The good news is, there has already been some progress in this matter. One of the latest trends in artificial intelligence is called no-code AI. We sincerely believe that sooner or later, artificial intelligence will become so advanced, you will be able to build models and algorithms with simple drag-and-drop builders, just like you can build WordPress websites today.

And then, we have something called AutoML 2.0[1]–a new approach to machine learning that allows you to automate and simplify many elements of developing a ready-made machine learning algorithm. So the good news is, the future looks bright!

Challenge #8: Deployment

Although it may sound amusing, many machine learning specialists struggle with the proper deployment of their projects. Sometimes, people working with ML have a hard time understanding business problems.

As a result, their algorithms, which in theory should deal with these challenges, frequently are disproportionate or inadequate, and the whole project is in vain. There is only one way to deal with this challenge. You need a team of experts that have not just machine learning qualifications but business qualifications as well. Only this way can you make sure your project will be useful from the business point of view.

Challenges related to machine learning applications

Lastly, we have two more machine learning challenges that refer to specific applications of its algorithms. Let’s take a closer look at them:

Challenge #9: Video training data

Today, the vast majority of machine learning models are trained on static data, e.g., pictures and texts. We still have a problem with using dynamic data to “teach” machine learning algorithms.

Imagine how future machine learning models will be advanced once we figure out the way to teach them through videos, sounds, and animations! This will be a major breakthrough that will allow us to achieve entirely new and maybe even unimaginable applications of

Challenge #10: Object detection

In theory, everything is cut and dried. Object detection is a feature that’s based almost exclusively on detecting various objects in images. Object detection is possible thanks to two other AI-related technologies: Deep learning and computer vision. Computer vision-based devices extract fragments of pictures and analyze them using deep learning in order to recognize patterns within them. One of the most advanced forms of object detection is face detection, mainly because human faces have many different features and, generally speaking, are quite similar.

machine learning models

The deep learning algorithms have to take all of these elements into account to correctly identify a given person. And although we know and understand how this technology works, object detection is still quite a challenge, and many algorithms struggle with it. Of course, our solutions are getting better and better at it, but there’s still a lot to achieve.

Interested in machine learning? Read our article: Machine Learning. What it is and why it is essential to business?

Challenges of machine learning – summary

Is that a complete list of machine learning challenges? By no means! These are the challenges that we find most pressing and urgent. Some of them will be solved within the next few months. Some will, unfortunately, have to wait a bit longer. But the direction is clear. We work tirelessly to make machine learning more accessible and effective.

As you can see, although machine learning is a tremendous technology, there’s still a lot to achieve. We still have some significant challenges that we have to overcome in order to develop this technology further. However, it doesn’t mean that you can’t use machine learning for the benefit of your company today! In fact, thanks to deep learning, we’ve been able to achieve some spectacular results!

Thanks to ML, companies all over the world work in a more effective and automated way. Intelligent machines and applications now execute many mundane tasks. If you’d like to find out how machine learning can help your company grow–you’re in the right place!

At Addepto, we provide machine learning consulting. Our role is to help clients find and implement the solution that suits their needs and helps them overcome their everyday challenges. Find out more today!

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See our machine learning consulting services to find out more.

What Are the Top 10 Challenges of Machine Learning? FAQ

What are the main challenges of machine learning?

Machine learning faces numerous challenges, including data quality issues, lack of interpretability, scalability problems, and ethical concerns, among others.

How does data quality affect machine learning projects?

Poor data quality can significantly impact the performance of machine learning algorithms, leading to inaccurate predictions and unreliable results. Ensuring high-quality data is crucial for successful ML projects.

What are some common scalability challenges in machine learning?

Scalability challenges arise when trying to deploy machine learning models to handle large datasets or high volumes of incoming data in real-time. Scaling ML algorithms to meet increasing demands while maintaining efficiency is a significant concern.

Why is interpretability important in machine learning?

Interpretability refers to the ability to understand and explain how a machine learning model makes decisions. It’s crucial for building trust in AI systems, ensuring fairness, and identifying potential biases.

How can bias be addressed in machine learning algorithms?

Bias in machine learning algorithms can be mitigated through techniques such as data preprocessing, algorithmic fairness measures, and diverse representation in training data.

What are the ethical challenges associated with machine learning?

Ethical challenges in machine learning include issues related to privacy, transparency, accountability, and the potential societal impacts of AI technologies. Addressing these concerns requires careful consideration of ethical principles and regulatory frameworks.

What role does MLOps play in overcoming challenges in machine learning projects?

MLOps, or machine learning operations, involves the deployment, monitoring, and management of machine learning models in production environments. It helps address challenges such as model drift, version control, and reproducibility, ensuring the reliability and effectiveness of ML systems.

How can organizations overcome challenges in implementing AI and machine learning solutions?

Organizations can overcome challenges by investing in skilled talent, adopting best practices in data management and governance, fostering a culture of experimentation and innovation, and staying informed about emerging technologies and trends in the field.

What are some key considerations for successful AI adoption in business?

Successful AI adoption requires clear strategic objectives, alignment with business goals, adequate resources and infrastructure, cross-functional collaboration, and continuous evaluation and refinement of AI applications to drive value and competitive advantage.

This article is an updated version of the publication from Oct 13, 2021.


[1] AutoML 2.0: Is The Data Scientist Obsolete?. URL: Accessed Oct 8, 2021.


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