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Top 7 Common Machine Learning Challenges Facing Business


Trinh Nguyen

May 02, 2024

Top 7 Common Machine Learning Challenges Facing Business

In this time of technological breakthroughs, artificial intelligence, particularly the machine learning industry, is one of the most rapidly growing technologies. It’s a pivotal force driving innovation, efficiency, and problem-solving across every economic sector and daily life aspect. Machine learning is applied in small businesses in numerous ways: automate decision-making processes, enhance personalization, and provide predictive analytics. It significantly increases efficiency and productivity, allowing humans to focus on more complex issues.

However, the path to harnessing machine learning’s full potential encounters difficulties, from data complexity to ethical dilemmas and security issues.

In this article, we’ll uncover the 7 most common machine learning challenges, along with providing a roadmap to overcome them, paving the way for successful integration and utilization of this transformative technology.

Data Challenges

Data Challenges of Machine Learnin

Data is the fuel that powers machine learning algorithms, and its quality and quantity significantly impact the model’s performance. That’s why you should keep the training dataset accurate, relevant, and diverse to ensure robust, effective, and unbiased model development.

In fact, there still remain some significant challenges in the data, from collecting the correct data to dealing with imbalanced datasets or addressing data bias.

#1 Lack of Data

Many businesses, especially small to SMEs, are experiencing a data shortage. They either can’t generate enough data internally, or their data collection processes are not comprehensive enough to capture the full spectrum of information needed. This scarcity poses a substantial barrier to implementing effective machine learning solutions that truly understand and predict complex patterns.


From the data science perspective, to overcome this challenge, businesses need to look beyond traditional data collection methods and explore alternative data sources. Public datasets offer a wealth of information across various domains. They provide a solid foundation for training machine learning models, especially when combined with a company’s internal data.

Customer surveys and feedback forms are another way businesses can enrich their datasets. This direct-from-the-source data gives insights into customer preferences, behaviors, and trends that aren’t captured through other means. Leveraging this information significantly enhances the machine learning models’ quality.

Social media data comes as a goldmine of consumer sentiment, trends, and preferences. Analyzing interactions on social channels allows businesses to understand their audience better and tailor their ML models to predict customer behavior more accurately.

The data augmentation technique is also highly recommended. It creates synthetic data based on the existing data points to increase the data volume for training machine learning models significantly.

#2 Poor Data Quality

Owning a vast amount of data, do you think you’re ready to train the model? It’s not that simple.

Quality data directly influences the accuracy and reliability of machine learning models. First, inconsistent formatting across datasets can confuse models, leading to incorrect interpretations and predictions. Next, missing values can compromise the dataset integrity, forcing models to make accurate predictions based on incomplete information. Also, inaccuracies, whether from human error or faulty data collection processes, can mislead models and result in flawed outcomes.


To increase the data quality, your businesses must prioritize data cleaning and pre-processing. These steps will identify and correct inaccuracies, fill in missing values, and standardize data formats. Investing in automated data cleaning tools can streamline this process, making it more efficient and less prone to human error. These tools can quickly scan large datasets, identify issues, and apply fixes according to predefined rules, ensuring consistency and accuracy across the board.

Partnering with skilled data scientists is another crucial strategy. These professionals possess the expertise to delve into the data, uncover underlying issues, and apply sophisticated techniques to clean and prepare the data for ML models. Their knowledge extends beyond simple cleaning procedures, allowing them to engineer features that enhance the model’s performance and ensure that the data truly represents the problem.

#3 Data Overfitting vs. Underfitting

Overfitting occurs when a machine learning model learns the training data too well, capturing noise and outliers as if they were significant trends. However, the model might perform exceptionally on the training data but poorly on any new, unseen data. It’s like memorizing the answers to a test without understanding the underlying principles, rendering the model ineffective in real-world machine learning applications.

On the other hand, if a model is underfitting, it fails to capture the underlying data patterns. This usually happens when the model is too simple to handle the complexity of the data or when there is insufficient training data. Underfit models run poorly even on the training data, indicating a fundamental inability to understand the data’s structure.


For overfitting, regularization techniques prove ideal. Regularization adds a penalty for complexity, preventing the model from becoming too complicated for the data it’s learning from. You can use L1 (Lasso) and L2 (Ridge) regularization methods to penalize the coefficients of the regression models.

Use cross-validation techniques to split the training quality data into smaller sets. You need to train the model on one set and validate it on another so it can generalize well to new data. Repeat this process multiple times with different sets for training and validation to ensure the model’s robustness.

When addressing underfitting, the solution often involves collecting more data or reconsidering the model choice. More data will provide richer information for the model to learn from, potentially capturing more complex patterns. Plus, simple models may not be able to learn the nuances of the data, so switching to a more sophisticated model with more parameters might capture the underlying trends more effectively.

In some cases, feature engineering—creating new input features based on existing ones—can also solve underfitting. Transforming or combining input data in new ways enables businesses to uncover and highlight fundamental patterns previously obscured, making it easier for the model to learn.

#4 Data Security Concerns

One of the most significant challenges of machine learning models is related to security. Data security in ML means protecting the data used for training and inference from unauthorized access and ensuring that the data handling practices comply with legal and ethical standards. Not only does it safeguard sensitive information, but it also maintains the model’s integrity.


Primarily, businesses must implement robust security protocols that encompass the entire data lifecycle, from collection and storage to processing and analyzing data. This includes complying with relevant data privacy regulations like the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA), which has strict guidelines for data handling and consumer rights.

Leveraging cloud platforms offers built-in security features that can significantly increase data protection efforts. These platforms typically have encryption services for data at rest and in transit to make the data unreadable to unauthorized users. Additionally, their comprehensive access controls permit businesses to define who can view specific data sets and under what conditions. As a result, your businesses can benefit from high levels of data protection without extending in-house security infrastructure.

Additionally, partner with companies prioritizing data security and ethical AI practices. They have expertise in securing data pipelines and making sure ML models are developed and deployed in a manner that respects privacy and ethical considerations.

Infrastructure and Implementation Challenges

Infrastructure and Implementation Challenges of machine learning

Once you have the data in order, it’s time to build the necessary infrastructure and implement your ML project. Here’s where some roadblocks might appear.

#5 Insufficient Infrastructure for Data Processing and Experimentation

A machine learning project, especially one involving deep learning algorithms, requires substantial computational resources for model training and inference. These processes can be incredibly data-intensive, affecting millions of calculations and data points. Without the proper infrastructure, businesses may struggle to move beyond the experimental phase.


It’d be great for businesses to turn to cloud-based solutions. Cloud platforms scale up computing power and storage, letting businesses adjust resources according to their ML workload demands.

Cloud providers typically have a range of services designed explicitly for ML and artificial intelligence initiatives. Businesses can leverage these cloud-based solutions to reduce the time and effort required for their machine learning projects.

Another advantage of cloud platforms is the availability of pre-built tools and services to streamline the ML model development and deployment. These tools will handle everything, from processing data and training models to deploying and monitoring them. These platforms, with a comprehensive suite of machine learning tools, enable businesses to focus on model development and experimentation rather than getting bogged down in infrastructure management.

#6 Time-consuming Implementation Process

Implementing machine learning may take your business a lot of time and effort. The process requires several stages: data collection, preparation, model training, testing, and deployment. Each proves time-intensive, particularly for teams new to ML or working with complex datasets. This challenge can lead to increased costs, delayed project timelines, and missed opportunities.


One effective strategy for efficient implementation is to break down the ML project into smaller, more manageable tasks. Teams can focus on specific aspects of the project, making identifying and resolving issues easier. You’ll achieve quick wins and maintain momentum and stakeholder confidence throughout the project lifecycle as well.

Moreover, use tools that automate repetitive and time-consuming steps within the ML workflow. Data preparation, for instance, is a critical yet laborious phase that needs cleaning, normalizing, and transforming data into a suitable format for model training. Automation tools can reduce the time spent on these tasks, letting data scientists and ML engineers focus on more strategic aspects.

For businesses looking to simplify the machine learning process further, low-code and AutoML (Automated Machine Learning) platforms turn out to be a compelling solution. These platforms abstract away the complexity associated with ML model development, making it accessible to non-experts and accelerating the implementation process.

Business Challenges

Business Challenges of Machine Learning

Apart from the model’s data and infrastructure, challenges remain in the business’s own capabilities to implement machine learning projects successfully.

#7 Lack of Skilled Machine Learning Engineers

As AI and machine learning continue to drive innovation across various industries, the demand for skilled data scientists and machine learning engineers has skyrocketed.

However, the supply of qualified professionals has not kept pace with this demand, leading to a significant talent gap. This shortage poses a considerable challenge for businesses looking to leverage ML technologies, as finding and recruiting the right talent can be difficult and costly.


One effective way is to partner with AI companies that provide expert machine learning services and pre-built solutions. Leading the AI solutions in Vietnam, Neurond grants access to a team of experienced ML engineers and data scientists, permitting businesses to bypass the talent acquisition hurdle. With great expertise in various fields, we can assist your business in developing and implementing machine learning solutions without hiring a whole in-house team. Additionally, we bring industry-specific knowledge and provide customized solutions tailored to a business’s unique needs.

Investing in training programs to upskill the existing workforce is worth your efforts to mitigate the talent shortage. You can provide employees with the opportunity to develop new skills or enhance existing ones to cultivate an in-house pool of machine learning talent. This approach addresses the immediate challenge of finding skilled ML wizards and contributes to employee satisfaction and retention.

Think of outsourcing specific machine learning tasks to freelancers or agencies specializing in ML. Businesses are able to tap into a global talent pool, where they can find individuals or teams with the precise skill set for a particular project. Outsourcing will benefit short-term projects or specific aspects of the ML development process, such as data annotation or model optimization.

Overcome Machine Learning Challenges

The successful machine learning journey is fraught with challenges. From data quality issues and ethical concerns to the sheer complexity of machine learning algorithms and the shortage of skilled professionals, businesses must navigate a labyrinth of obstacles.

However, these challenges are not insurmountable. By understanding the common hurdles and implementing the suggested solutions, businesses can position themselves to leverage the transformative power of ML effectively.

If you have a question or concern about machine learning challenges or how to overcome them, don’t hesitate to contact us.