Top 9 Machine Learning Challenges in 2024

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Mateusz Opala

Updated Jan 25, 2024 • 13 min read
The main challenges of in ML projects

Although scientists, engineers, and business mavens agree we might have finally entered the golden age of artificial intelligence you have to be ready to face much more challenges than you think when planning a machine learning project.

Entrepreneurs, designers, and managers tend to overestimate the present capabilities of machine learning. They expect the algorithms to learn quickly and deliver accurate predictions to complex queries. They expect wizardry.

Commercial use of machine learning, especially deep learning methods, is relatively new. They require vast sets of properly organized and prepared training data to provide accurate answers to the questions we want to ask them. A business working on a practical machine learning application needs to invest time, resources, and take substantial risks.

A typical artificial neural network has millions of parameters; some can have hundreds of millions. A training set usually consists of tens of thousands of records. While a network is capable of remembering the training set and giving answers with 100 percent accuracy, it may prove completely useless when given new training data. It is just one of the limits to current deep learning algorithms. Read on top 9 challenges for machine learning projects.

The black box problem

The early stages of machine learning belonged to relatively simple, shallow methods. For example, a decision tree algorithm acted strictly according to the rules its supervisors taught it: "if something is oval and green, there's a probability P it's a cucumber." These machine learning models weren't very good at identifying a cucumber in a picture, but at least everyone knew how they work.

Deep Learning algorithms are different. They build a hierarchical representation of data - layers that allow them to create their own understanding. After analyzing large sets of training data, neural networks can learn how to recognize cucumbers with astounding accuracy. The problem is that their supervisors - the machine learning engineers or data scientists - don't know exactly how they do it. The problem is called a black box.

Artificial Intelligence supervisors understand the input (the training data that the machine learning algorithm analyses) and the output (the decision it makes). While the engineers are able to understand how a single prediction was made, it is very difficult to understand how the whole model works.

Some AI researchers, agree with Google's Ali Rahimi, who claims that machine learning has recently become a new form of "alchemy", and the entire field has become a black box. It is a significant obstacle in the development of other AI applications like medicine, driverless cars, or automatic assessment of credit rating. What if an algorithm’s diagnosis is wrong? How will a car manufacturer explain the behavior of the autopilot when a fatal accident happens? How will a bank answer a customer’s complaint?

The black box is an issue in machine learning for in-app recommendation services. It turns out that web application users feel more comfortable when they know more or less how the automatic suggestions work. That is why many big data companies, like Netflix, reveal some of their trade secrets.

The research shows artificial intelligence usually causes fear and other negative emotions in people. People are afraid of an object looking and behaving "almost like a human." The phenomenon is called "uncanny valley".

We accept machines that act like machines, but not the ones that do the human stuff, like talking, smiling, singing or painting. Of course, this may change with time, as new generations grow up in a digital environment, where they interact with robots and machine learning algorithms.

Talent deficit

Although many people are attracted to the machine learning industry, there are still very few specialists that can develop this technology and that is currently one of common machine learning problems. A good data scientist who understands machine learning hardly ever has sufficient knowledge of software engineering.

The problem is drastic. The Chinese tech giant Tencent estimated at the end of 2017 that there were just about 300,000 researchers and practitioners dealing with AI worldwide. Element AI, nn independent company, estimates that "fewer than 10,000 people have the ML skills necessary to tackle serious artificial intelligence research". Machine learning engineers and data scientists are top priority recruits for the most prominent players such as Google, Amazon, Microsoft, or Facebook.

It makes salaries in artificial intelligence field skyrocket, but also makes the average quality of specialists available on the market plummet. With machine learning, the problem seems to be much worse.

According to DataCamp, average salary for ML engineers in 2023 raised up to $160,140, while the best will get as much as NBA superstars. In a court filing in 2016, Google revealed that one of the leaders of its self-driving-car division earned $120 million in incentives before he left for Google's competitor - Uber.

Data is not free at all in machine learning projects

As I mentioned above, to train a machine learning model, you need big sets of data. It may seem that it's not a problem anymore, since everyone can afford to store and process petabytes of information. While storage may be cheap, it requires time to collect a sufficient amount of training data. Moreover, buying ready sets of data is expensive.

There are problems in machine learning of a different nature. Preparing data for algorithm training is a complicated process. You need to know what issue you want your machine learning algorithm to solve, because you will need to plan classification, clustering, regression, and ranking ahead.

You need to establish data collection mechanisms and consistent formatting. Then you have to reduce data with attribute sampling, record sampling, or aggregating. You need to decompose the training data and rescale it. It is a complex task that requires skilled engineers and time. So even if you have infinite disk space, the process is expensive.

If you plan to use personal data, you will probably face additional challenges. People around the world are more and more aware of the importance of protecting their privacy. They may be unwilling to share them with you or issue a formal complaint if when they realize you did it, even if you obtained all they gave you their consent.

Personal data and big data activities have also become more difficult, risky and costly with the introduction of new regulations protecting personal data, such as the famous European General Data Protection Regulation.

The machine learning technology is very young

Once again, from the outside, it looks like a fairytale. The biggest tech corporations are spending money on open source frameworks for everyone. The Alphabet Inc. (former Google) offers TensorFlow, while Microsoft cooperates with Facebook developing Open Neural Network Exchange (ONNX). These systems are powered by data provided by business and individual users all around the world.

However, the central problem of machine learning is that all these environments are very young. The first version of TensorFlow was released in February 2017, while PyTorch, another popular library, came out in October 2017. Web application frameworks are much, much older - Ruby on Rails is 20 years old, and the Python-based Django is 27 years old.

On one hand young technology uses the most contemporary solutions, on the other, it may not be production-ready, or be borderline production ready.

You need time to achieve any satisfying results and planning is difficult

Traditional enterprise software development is pretty straightforward. You have your business goals, functionalities, choose technology to build it, and assume it will take some months to release a working version. In machine learning development has more layers. The engineers are writing a program that will generate a program, which will learn to perform the actions you planned when setting your business goals. Just adding these one or two levels makes everything much more complicated.

The challenge is that machine learning takes much more time. You have to gather and prepare training data, then train the algorithm. There are much more uncertainties. That is why, while in traditional website or application development an experienced team can estimate the time quite precisely, a machine learning project used for example to provide product recommendations can take much less or much more time than expected. Why? Because even the best machine learning engineers don't know how the deep learning networks will behave when analyzing different sets of data. It also means that the machine learning engineers and data scientists cannot guarantee that the training process of a model can be replicated.

In the realm of machine learning challenges, navigating the intricate landscape of time and planning presents a formidable hurdle for businesses venturing into the realm of data science and machine learning applications. The machine learning process involves a series of crucial stages, from analyzing data and selecting appropriate machine learning techniques to model training and deployment.

To address this, it is essential to break down these stages and offer realistic time estimates for each, ensuring that expectations align with the intricacies of the machine learning journey. Project management methodologies tailored to machine learning development play a pivotal role, emphasizing flexibility to adapt to the dynamic nature of data-driven projects.

Moreover, highlighting the importance of continuous testing and validation throughout the machine learning lifecycle becomes imperative, safeguarding against unforeseen challenges and ensuring the robustness of the models employed in various machine learning applications.

Data quality and quantity

A big challenge also lies in the domain of data quality and quantity. Attaining high-quality and sufficient training data is a formidable task, marked by the presence of noisy data and the potential pitfalls of a poor-quality dataset.

The reliability and accuracy of machine learning models hinge on the quality of the data they are trained on, and navigating through issues such as outliers, missing values, and inaccuracies becomes paramount.

Strategies for handling imbalanced datasets, where certain classes or outcomes are underrepresented, must be carefully considered. Moreover, addressing biases in training data is crucial to ensure fair and equitable model predictions, especially in applications that impact diverse user groups.

As businesses embark on their machine learning journey, grappling with these challenges necessitates a strategic approach to data curation, encompassing data cleaning, preprocessing, and augmentation techniques to enhance both the quality and quantity of the training dataset.

Model interpretability

In the expansive realm of machine learning, ensuring the interpretability of models presents a crucial challenge, especially in sensitive sectors like healthcare and finance.

The significance of interpretable models cannot be overstated, given the need for accurate and understandable insights in decision-making processes. While employing complex mathematical calculations and advanced algorithms can yield precise results, it introduces a trade-off between model accuracy and interpretability.

Striking the right balance is pivotal, particularly when transparency is vital for gaining trust and comprehension from users and stakeholders. In the pursuit of training models, the choice between a complex, non-linear model and a simpler linear model requires careful consideration.

Businesses in the ever-evolving landscape of machine learning must conscientiously weigh these factors to ensure that deployed models not only deliver accuracy but also maintain transparency and comprehensibility, fostering trust in one of the most rapidly growing and technologically advanced fields.

Scalability issues

In the vast world of machine learning, a significant challenge surfaces when it comes to scalability, especially with large datasets or complex data structures. Scaling machine learning models to handle this wealth of information requires a thoughtful strategy to overcome inherent obstacles.

A deep understanding of scalable solutions, such as distributed computing and parallel processing, becomes essential. These technologies play a crucial role in efficiently managing extensive datasets and intricate mathematical computations.

In the ever-evolving real world, where new data continuously emerges, businesses grapple with the need for scalable approaches. Whether it's adapting to robotic training data or navigating the complexities of rapidly advancing technologies, a nuanced understanding and implementation of scalable solutions are paramount. It's about staying agile and effective in a landscape shaped by the most rapidly growing technologies, ensuring that machine learning seamlessly integrates with the dynamic demands of today's data-rich environments.

Regulatory compliance

Within the realm of machine learning challenges, navigating regulatory compliance emerges as a critical hurdle, particularly in industries characterized by stringent standards.

The amalgamation of quality data and compliance becomes a focal point, especially when dealing with sensitive domains like medical diagnosis or video surveillance. Ensuring data security is paramount in adhering to regulatory frameworks, where the fantastic technology of speech recognition or the complexities of historical and incomplete data must align with legal and ethical standards.

Careful examination of the regulatory landscape becomes imperative, as industries face the excessive requirements placed upon machine learning applications. Strategies for achieving compliance involve not only meeting legal and regulatory standards but also developing a robust framework that safeguards against potential breaches.

As machine learning continues to shape industries, proactive approaches to regulatory challenges are essential to foster trust and ethical use of this transformative technology.

Understand the limits of contemporary machine learning technology

It's very likely machine learning will soon reach the point when it's a common technology but the main machine learning problems are yet to be solved. Nevertheless, engaging in an AI project is a high risk, high reward enterprise. You need to be patient, plan carefully, respect the challenges machine learning technology brings, and find people who truly understand machine learning and are not trying to sell you an empty promise.

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