The application of machine learning (ML) in various industries has enabled technological advances and ground-breaking inventions, from prediction engines and personalized recommendations to detecting diseases and automatic driving. The ML market is anticipated to increase from about $140 billion to almost $2 trillion by 2030.
This article shares the expertise of Unicsoft, a software development company specializing in machine learning consulting, to review applications of ML in different sectors. You will learn how and what machine learning is used for, its significant benefits, and its notable applications. We will review Unicsoft’s cases, such as Supply Chain Predictive Analytics and Retail Category Management, to demonstrate real-life examples of successful integration of machine learning in business.
How Does Machine Learning Work?
Machine learning is a subset of artificial intelligence that enables computers to learn from experience without explicit human intervention. Using their own algorithms, machines can analyze data, identify patterns, and make predictions or decisions based on the insights gained. The algorithms’ performance and accuracy increase as more data becomes accessible.
Three primary areas of machine learning are:
- Supervised learning: Algorithms learn from labeled data to predict or classify new, unseen examples.
Unsupervised learning: Algorithms uncover patterns and relationships within unlabeled data, aiding in clustering and data exploration.
- Reinforcement learning: Agents interact with environments, learning optimal actions through trial and error to achieve specific goals.
ML algorithms require input data and corresponding output labels (the desired outcome or prediction) in supervised learning. This data is fed into the ML model during the training phase. Data preparation in machine learning helps to improve ML models and achieve breakthroughs in various industries, from healthcare and finance to marketing and entertainment.
Benefits of Machine Learning
Machine learning offers many benefits that have transformed industries and businesses worldwide. Some key advantages include:
- Automation: Machine learning allows process automation, reduces the need for manual intervention, and speeds up tasks.
- Less Reliance on Human Interaction: Businesses can optimize resource allocation and reduce human errors by automating repetitive tasks with ML algorithms.
- Scale of Improvement: Machine learning algorithms continuously learn and adapt, building their own knowledge base and functionality.
- Efficient Data Processing: Machine learning can analyze different types of data, providing valuable insights from structured and unstructured datasets.
- Wide Range of Applications: Machine learning is revolutionizing business operations in most industries, from healthcare to finance and retail.
What Is Machine Learning Used For?
Organizations apply machine learning in diverse fields, enhancing various processes and decision-making. Some common uses of machine learning include:
- Image Recognition: ML-enabled computer vision helps to identify and classify objects or patterns within images. Supervised learning applications power technologies like facial recognition, image-based search, and object detection in design diagrams.
- Medical Diagnostics: Machine learning is used in medicine to analyze images and predict patient outcomes, assisting in diagnosing some diseases and genetic disorders.
- Automated Driving: Self-driving cars leverage machine learning to process sensor data and make real-time decisions for safe navigation.
- Text Generation and Analysis: Natural Language Processing (NLP) allows machines to understand, generate, and analyze human language, facilitating content generation and text correction.
- Speech Recognition: Machine learning enables accurate speech recognition, interpretation, and speech-to-text automation.
- Data Analytics: ML algorithms help businesses analyze large datasets, extract valuable insights, and make data-driven decisions.
- Algorithmic Recommendations: E-commerce platforms and entertainment services use machine learning to personalize recommendations based on user preferences.
When to Use Machine Learning?
Machine learning excels when traditional methods fail to process large amounts of statistical data. Machine learning algorithms can identify patterns and relationships in extensive datasets to provide insightful information when simple rules or calculations cannot forecast a target value effectively.
Where machine learning is used? It is frequently applied to forecast results or identify trends. Machine learning works best when data is structured or categorized. It is beneficial for organizing and analyzing videos, photos, and audio files. ML algorithms can quickly classify and evaluate this type of data. For example, they detect faces in a snapshot.
12 Successful Machine Learning Applications and Examples
Machine learning is applied not only in tech-heavy businesses but across various industries. In this section, we will identify the successful applications of ML in different sectors.
Machine Learning in E-Commerce
Machine learning applications in industry help businesses predict customer behavior, optimize pricing strategies, and improve the overall customer experience. ML algorithms help e-commerce companies provide personalized product recommendations, targeted marketing campaigns, and efficient inventory management.
ML-powered recommendation engines, fraud detectors, and customer support chatbots improve the eCommerce experience for buyers and sellers. They enable e-commerce giants like Amazon and Alibaba to keep growing, encouraging users to return to the website for quick and easy purchases. Small e-commerce players can also improve their performance with Amazon’s ML-approved recommendation engine and customer segmentation accessible over AWS. Here is our recent project on ML-powered anti-fraud system for e-commerce users.
To learn more about how machine learning enhances e-commerce, check out our blog post on Powerful Ways to Use Machine Learning in E-commerce.
Machine Learning in Fraud Detection Strategy
Machine learning application helps to develop a fraud detection model, improve identity verification, or spot suspicious patterns. ML algorithms for fraud detection process big historical and real-time datasets. They use numerous factors to look for implicit and explicit correlations between user behavior and the potential for fraudulent behavior.
With ML algorithms, organizations can quickly identify suspicious transactions, conduct anti-fraud analyses without compromising the UX, and minimize losses. Examples of machine learning for fraud detection include credit card fraud prevention, Point-of-Sale (POS) anomaly detection, account takeover prevention, behavioral biometrics, and invoice fraud prevention.
Read more about using machine learning to strengthen your fraud detection strategy in our blog post on Making Use of Machine Learning in Fraud Detection Strategy: Tips & Tricks.
Machine Learning in Retail
ML business applications help retailers to synthesize vast amounts of customer data that are impossible to understand by human analysis. ML-powered personalized recommendations, dynamic pricing, customer feedback analysis, chatbots, category management, and inventory forecasting revolutionize the retail industry.
Applying machine learning in retail enhances the customer experience, optimizes supply chain management, increases sales, and reduces churn rates. It helps retailers retain clients and attract new ones, save time and resources, prevent fraud, and gain a reputation as forward-thinking merchants that value their clients and go the extra mile to make them happy.
Learn more about the Application of Machine Learning in Retail in our blog post.
Machine Learning to Predict Retail Demand
Accurate demand forecasting optimizes inventory levels and reduces costs. Retailers use machine learning to consider various variables and relationships impacting daily demand. Machine learning models analyze historical sales data and external factors to predict future demand, avoiding excessive stockpiling.
Machine learning algorithms create continuously improving models to analyze large-scale data sets from numerous sources and predict which products are needed for each location on any given day. They measure repeating patterns, internal company actions, and external elements like weather, regional events, and rival activity. It ensures precise, detailed, and automatic short- and long-term demand predictions, resulting in high availability and minimal stock risk.
Read our blog post on How to Improve Retail Demand Forecasting with Machine Learning to discover more.
Machine Learning for Anomaly Detection
ML-powered anomaly detection identifies unusual patterns or behaviors in various fields, such as cybersecurity and manufacturing. ML solutions can quickly process large amounts of data to derive real-time insights and detect anomalies, enabling timely responses and preventing potential threats.
Some ML models become increasingly accurate at spotting outliers with each new example, identifying new fraudulent schemes. Unsupervised ML models can learn from data that hasn’t been explicitly categorized as “normal” or “anomalous” and spot irregularities that humans cannot. Find out more about the role of machine learning in the field in our blog post on Anomaly Detection in Machine Learning: How It Can Help Your Business.
Machine Learning in Supply Chain Management
Machine learning optimizes supply chain management by predicting demand, streamlining logistics, and reducing lead times. Businesses achieve greater efficiency and cost-effectiveness in their supply chain operations. ML technologies also enable real-time tracking and monitoring of shipments, driving proactive problem-solving and enhancing customer satisfaction.
Machine learning increases precision and responsiveness throughout the supply chain by automating repetitive processes, spotting inefficiencies, and adjusting to changing market conditions to save costs. Read our blog post on the Application of Machine Learning in Supply Chain for more insights.
Machine Learning for Insurance
Machine learning helps assess risks and automate claim processing in insurance. You can build an ML-based model to analyze customer data, determine policy risks, and identify potential fraud cases, improving the overall efficiency of insurance companies. Examples of ML applications for insurance companies include personalization, estimation of insurance risks, automatic recognition of documents and damaged machines, and potential repair cost calculation.
Learn more about the transformative impact of machine learning in our blog post on How Machine Learning Is Reforming the Insurance Industry.
Machine Learning in Finance
Machine learning enables better risk assessment, fraud detection, and customer service. Analysts in the financial services industry use machine learning to automate trading processes, spot fraud, analyze market trends, predict stock prices, and make data-driven investment decisions.
Banking companies can use ML algorithms to determine whether a transaction is suspicious compared to other data points. For example, they can identify whether a $500 Amazon purchase was something a particular client would likely do. Additionally, ML-based financial robo-advisors offer support in portfolio management based on the investors’ goals, risk tolerance, and other characteristics.
Machine Learning in Healthcare
Machine learning is used to personalize medical treatments, expedite administrative procedures, enhance patient care, and improve disease diagnosis, from medical imaging analysis to personalized treatment plans. For example, technology like PathAI uses machine learning to connect patients with new therapies or treatments.
ML algorithms can also predict heart failure by examining doctors’ e-notes and spotting similarities in a patient’s cardiovascular history. Additionally, machine learning algorithms can predict how long patients need to wait in the emergency rooms in hospital departments. Read more about Top 5 ML use cases in healthcare in our blog.
Machine Learning in Education
Machine learning benefits both process-based chores and student-focused initiatives. Applying ML in education facilitates adaptive and inclusive learning, personalized tutoring, performance assessment, real-time translation, task automation, and chatbots for student support. Commercial educational institutions and eLearning platforms use machine learning to target potential students with tailored advertisements to encourage subscriptions and matriculation.
Machine Learning in CyberSecurity
ML algorithms in cybersecurity detect malware and malicious activities, such as phishing attempts, malware, and ransomware, that would otherwise go unnoticed. Additionally, they can be used to model typical user behavior so that deviations can be found and looked into.
Machine learning models analyze network traffic, identify potential threats, and adapt to new attack patterns, enhancing the overall security of digital systems. The use cases for machine learning in the cybersecurity industry include vulnerability management, static and behavioral analysis, anomaly detection, forensic analysis, and sandbox malware analysis.
Machine Learning Examples by Company
Companies of all sizes use machine learning to increase their product offerings and improve their workflows. Below are several machine learning examples in business.
In 2020, Meta introduced a Facebook chatbot named Blender that could communicate on various subjects instead of only a predetermined list of topics. The MIT Technology Review claims this bot was “first trained on 1.5 billion publicly available Reddit conversations.” Then, the company improved its focus on emotional talks, chats with vast data, and conversations between users with different personalities.
Amazon leverages ML-enabled product recommendations. Once you explore and shop on Amazon, you will see suggestions like “Customers Who Bought This Product Also Bought.” These are the outcomes of machine learning algorithms, which classify both merchandise and consumers to make recommendations to users.
Wanda is an ML-powered remote patient monitoring platform built by the Unicsoft expert team. It is an example of machine learning that connects doctors, clinicians, patients, and caregivers. The solution utilizes machine learning algorithms, prediction, and risk analytics to enhance care plans through timely alerts to doctors, preventive actions for patient well-being, and efficient communication among care team members.
At Unicsoft, we provide tailored ML solutions that address your business requirements and deliver ground-breaking outcomes. Our dedicated team of professionals has successfully completed predictive analytics, anomaly detection, and other ML projects. Machine learning consulting helps to identify how your company can benefit from ML technologies and create a road map for developing and deploying ML solutions.
For businesses of any size, Unicsoft helps to minimize the risks connected to the challenges of ML solution development, such as data cleanliness, confidentiality, time-consuming implementation, and high engineering requirements. We assist in identifying potential issues during the consultation stage and offer you the best option depending on your unique situation.
Look at our success cases.
Supply Chain Predictive Analytics
Our client, a leading supply chain software and service provider, hired the Unicsoft team to optimize Supply Chain Predictive Analytics with ML-powered automation of exception prediction and data processing from different suppliers. We have significantly reworked the machine learning model for logistics and supplier-related incident prediction. As a result, a client obtained:
- Better forecasting precision (80%);
- A modern, user-friendly interface built from scratch;
- Report generation in various formats and required regularity.
AI-Driven Solution for Credit Risk Assessment
In the ever-evolving landscape of financial services, our cutting-edge AI-driven Solution for Credit Risk Assessment from Unicsoft stands as a beacon of innovation. By leveraging the power of advanced machine learning algorithms and predictive analytics, we empower financial institutions to make smarter lending decisions with unprecedented accuracy. Through real-time data analysis and pattern recognition, our solution not only minimizes risk exposure but also maximizes the efficiency of credit evaluation processes.
With this solution in action, financial institutions can witness a remarkable reduction in default rates and the time required for loan approval, leading to enhanced customer satisfaction and accelerated business growth.
Mobile Hyper-Personalization in the Edge Device
Enter the realm of AI-driven future with our Mobile hyper-personalization solution on edge devices. In an era where user expectations for personalized experiences are at an all-time high, our cutting-edge approach employs real-time data processing and machine learning to deliver tailor-made content and recommendations directly to users’ mobile devices. This not only enhances user engagement but also opens up new avenues for businesses to connect with their customers on a deeper level.
The solution provides retail companies with remarkable benefits & business values:
- 6X CTR by engaging with your customers in real-time
- 3X CVR by leveling up to device real-world personalization & segmentation
- 2X CTR by increasing push deliverability
- +30% retention by identifying & targeting users at risk on day-1 of app installation
- -20% сhurn by targeting all users with the same level of personalization.
Retail Category Management
Our client, the largest retailer in Central and Eastern Europe, asked Unicsoft to develop a Retail Category Management tool. The client had over 50000 price look-up (PLUs) codes and dozens of new ones added daily. Unicsoft involved highly qualified Data Science and Data Warehouse (DWH) specialists to develop and support the tool.
After nine months of using the tool, the client saved significant money by eliminating staff who manually performed category management. The Unicsoft support team maintains analytical models to update them and obtain the best accuracy. The client noticed the high quality of the solution and Unicsoft’s delivery process and expressed a willingness for further collaboration.
Harness the Power of Machine Learning for Your Business with Unicsoft
Machine learning has become an essential tool across various industries, enabling businesses to automate processes, process data efficiently, make data-driven decisions, and enhance customer experience. We explored the top 12 machine learning applications in business, supported by real-life cases and examples, to demonstrate the potential of this technology.
Unicsoft is here to help you leverage machine learning technologies for your business. We develop custom ML solutions that meet your company’s needs and yield ground-breaking results. With our machine learning consulting, you will discover all the business benefits of the technology and get a road map for developing and implementing ML solutions.
Contact us today to consult deeper on your ML solution development and implementation.
Why is machine learning important?
Using machine learning for business helps to automate tasks, optimize processes, and gain valuable insights from vast datasets without explicit programming.
How do we create a machine learning model?
Creating a machine learning model involves several steps: data collection, preprocessing, feature engineering, model selection, training, and evaluation.
Why do we use machine learning?
Uses of ML help to gain valuable insights from vast amounts of data which is impossible with traditional rule-based approaches.
What are machine learning algorithms?
ML algorithms are mathematical models that analyze data, identify patterns, and make predictions or decisions in daily life.
In what industries is machine learning most commonly used?
AI ML is used in various industries, including medicine, finance, fintech, retail, cybersecurity, education, and more.