Machine learning enhances the accuracy and precision of various tasks by processing large amounts of data and identifying patterns. It’s one of the reasons why, in 2022, the machine learning market was valued at $19.2 billion and is expected to grow to $225.91 billion by 2030, as stated by Fortune Business Insights.
If your organization is considering implementing a machine learning-based solution, we encourage you to explore the advantages and benefits of ML and discuss some potential limitations and what you can do to minimize them.
We have also included a few of our success stories to demonstrate that, with the right approach, organizations can gain much more from ML than just getting work done faster.
The meaning of ML in business
Machine learning empowers businesses by providing actionable insights, which are used to streamline data-driven operations and optimize data platforms.
When ML models are engineered correctly, they can efficiently analyze large datasets to uncover subtle patterns that conventional methods may overlook. These capabilities show promise in fields such as:
- Medical diagnosis
- Financial forecasting
- Quality control in manufacturing
- Energy management
- Network traffic management and cybersecurity
- Predictive maintenance
Moreover, machine learning automates routine tasks, enabling employees to focus on more complex projects, which increases efficiency and reduces labor costs. However, achieving the desired outcomes depends on careful implementation and domain expertise.
Improved accuracy
Machine learning models can be trained to adapt to specific datasets and understand the underlying patterns within those datasets, making tasks like forecasting, fraud detection, and risk assessment more accurate.
High adaptability
With some customization, machine learning models can adapt to any environment. For example, a machine learning algorithm can analyze network traffic for a small business, but it also has the capacity to be used at a large-scale research institution like CERN.
While there are clear differences between these two organizations and the tasks the algorithm will perform, ML models can learn these distinctions—understanding what is normal behavior for a small business and an institution like CERN.
Machine learning adapts not only to institutions of different sizes but also to the most niche applications. For example, one of our clients assembles air-blowing equipment that plays a key role in manufacturing. Although their production process is highly efficient, it generates a large amount of metal scrap. The company aimed to reduce scrap waste by at least 1%, which would yield an estimated $330K in annual savings.
Our engineers built an ML model based on the client’s expertise and implemented a machine learning-driven data analytics solution. This achieved a 4.4% reduction in the client's scrap rate, significantly improving manufacturing efficiency.
Personalization and improved user experience
Machine learning models can analyze user behavior and personalize their experience with your product. The application of such insights is broad and can include:
- Customized recommendations (for example, if your product is a streaming service).
- Dynamic pricing.
- Personalized ads (like on Facebook).
- Predicting user problems and proposing solutions before the user needs to contact customer support.
Personalization and improved experience are important in e-commerce, where even the smallest hindrance can prevent users from making a purchase. In one of our projects, we helped our client harness the power of real-time personalization for their e-commerce platform. The platform was already powered by machine learning and used customer data to analyze user preferences and offer tailored product recommendations. However, the recommendations were delayed significantly, leading to missed sales opportunities.
Our ML engineering team focused on deploying and scaling an existing ML-based recommendation system using Apache Flink on Kubernetes for real-time data processing. They also used Spark batch technology to process data. This allowed them to reduce the time required to display personalized product recommendations from one week to just 15 minutes.
Below, we have listed some of the most common and and impactful advantages and benefits of machine learning.
Learning capability
One of the key advantages of machine learning models—as the name suggests—is their ability to learn. These models can improve over time and without explicit reprogramming, simply by learning from the latest data. In this context, there are three different types of learning:
- Supervised learning – The model learns from labeled data, where each input is paired with a known output.
- Unsupervised learning – The model identifies patterns and structures in data without predefined labels, such as in clustering tasks.
- Reinforcement learning – The model learns through interaction with its environment, receiving positive or negative feedback based on its actions to optimize decision-making.
Improved decision-making
Machine learning supports better decision-making in organizations through predictive models. These models identify trends, correlations, and patterns within large datasets and provide information that is then used to improve the efficiency of various operations and resource management.
Increased efficiency through automation
Machine learning models are able to automate repetitive tasks such as data entry, quality control, anomaly detection, monitoring, and image labeling. This automation allows for reducing human effort and boosting productivity and efficiency.
Automation can take many forms and be applied in any industry. For example, one of our clients, a large retailer, had a functional machine learning (ML) pipeline that generated insights based on user behavior data. However, our client lacked the engineering expertise and manpower necessary to scale up production, so they turned to us for help automating that ML pipeline.
Our engineers built a fully automated, end-to-end machine learning process that delivers new models on demand. They achieved this by leveraging multiple technologies to create small, manageable code pipelines. This approach made it easier for the client’s developers to understand individual data transformations, features, and models.
The complete machine learning process enabled the client's data science team to successfully produce 25 ML models and over 200 pipelines, significantly improving the personalization of the online shopping experience.
Scalability
A healthy, growing organization may find itself with a dataset so large that its current data management method can no longer keep up. This is not the case with ML models, whose efficiency remains consistent regardless of dataset size. As a result, businesses can focus on scaling their operations without worrying that growth will impact the efficiency of data management.
Our example of achieving scalability with machine learning
Our retail client collects and processes data through a batch learning approach, which became inefficient as their database kept increasing in size. They needed a new infrastructure for handling and training complex ML models.
Our engineers suggested a strategy based on a hybrid cloud and batch offline processing. To build this infrastructure, they utilized the Azure Machine Learning service, ETL pipelines, and feature engineering.
With new infrastructure, our client was able to collect several hundred terabytes of data from 5 million customers and use it to train their ML models with real-time adaptability to customer behavior.
Wide application
Based on our experience, ML can be used in a wide variety of industries, such as Industry 4.0, logistics, and retail. It can increase sales through better customization and improve manufacturing productivity through better data analysis.
A notable mention goes to ML’s role in cybersecurity, anomaly detection, and fraud prevention. ML algorithms can be trained to monitor and analyze large volumes of traffic in real time, allowing for the detection of potential threats before they cause any damage.
The potential benefits of machine learning come with trade-offs that every organization must consider. These trade-offs vary by organization, but many can be avoided by gradually implementing machine learning initiatives under expert supervision rather than through a large-scale transition.
High cost of setup and maintenance
The total cost of building and maintaining the infrastructure necessary for ML models to be efficient can sometimes outweigh the potential cost savings, especially if the transition to ML was rapid.
Before producing any meaningful insights, ML models must first ingest, analyze, and learn from large datasets. This takes time and computational resources, like processing power or storage space, which then creates a hardware requirement that the organization needs to address.
For example, some models, such as neural networks or XGBoost, require high GPU resources for efficient training. They also need their training cluster, either in the cloud or on-premises, which can significantly increase project costs. These clusters also require constant administration and maintenance, further adding to the overall expense.
Additionally, launching ML projects without the necessary in-house expertise can be challenging. To maintain and troubleshoot ML models efficiently, organizations often need to establish a specialized department, which creates additional costs.
You can mitigate these costs and risks by making sure that the transition and setup of the machine learning initiative are done gradually and with the oversight of experts with experience with such projects.
High barriers to entry
The biggest challenge in defining an ML project and its results is knowing what type of business problem you want to solve. Organizations should define their business problem in ML language with a proper expert, which can help them choose the right metric and define proper KPIs.
Without that proper definition, the ML model would generate additional costs, despite the right clusters and the best data.
For example, if a medical organization aimed to use its ML model to analyze images of CT scans to look for potential tumors, they could do so, and as their KPI set the 90% detection rate. Sounds reasonable, right?
The problem with that KPI is that its effectiveness will depend on the datasets used for training. Let’s say an organization uses a test dataset where only 10% of the images show tumors. In that case, if their ML model predicts that none of the images contain a tumor, it would still meet the 90% detection rate KPI, despite missing the tumor in 10% of images.
The same applies to a training dataset where 100% of the images show tumors. In that case, the model would recognize all of them and meet the KPI. However, due to the nature of the dataset, the ML model would always predict that a CT scan contains a tumor because it wouldn't know what an image without one looks like.
While both of these examples are edge cases, they present problems that could have been avoided if the right expert consultant had designed the ML model.
Data requirements
Machine learning models require massive amounts of data for training before they can produce reasonable and useful results. This presents a challenge for small and medium-sized organizations that are still growing their databases.
Similarly, data quality plays a crucial role. Machine learning models trained on poor-quality data are likely to produce subpar results.
Susceptibility to errors
Despite their autonomous nature, ML algorithms are prone to errors. The wrong choice of model or poor-quality data can lead to inaccurate results. To minimize these risks, organizations need to ensure high-quality inputs and appropriate model selection.
Challenging interpretation due to lack of transparency
The black-box nature of deep learning models makes the decision-making process more challenging, as organizations can sometimes struggle with grasping the logic behind decisions suggested by ML models.
It also poses challenges in fields requiring accountability, which inspires an ongoing discussion on the importance of efforts to develop more explainable ML models.
In order to generate clear and actionable insights, data analysts need to select the right algorithm and approach for the intended use case. Without transparency of the process, expertise, and careful design, these results may be challenging to understand or apply.
Machine learning continues to evolve, offering significant opportunities for improvement and innovation. It plays a crucial role in many industries and research fields, enabling businesses to adapt to market changes and evolving technical requirements while identifying and capitalizing on new opportunities.
Organizations must keep in mind that machine learning works best when implemented gradually, as it allows them to determine the best development strategy. It is also a good tool for testing and verifying PoCs.
Over time, organizations may uncover new advantages of machine learning that have yet to be considered. For now, it is essential to explore its various possibilities across different organizations, maximizing the potential of this technology in multiple contexts.
