Machine learning, popularly known as ML, is a derivative of artificial intelligence (AI) that extracts actionable insights from raw data in order to solve complex business problems with ease. ML algorithms continuously analyze data in order to ‘learn’ from it and find insights without having to be explicitly programmed for this purpose. In business, machine learning software enhances scalability and improves operations. ML algorithms are also rapidly gaining popularity in the business analytics segment. When implemented correctly, ML solutions can solve a variety of business problems. The fact that technology titans such as Amazon, Google, and Microsoft are investing so heavily in machine learning is a testament to its popularity and potential.
Below are a few key applications of machine learning in business.
Corrective maintenance is the traditional approach to the maintenance of machinery, vehicles, and other technology used in various industries. However, this approach is often inefficient and expensive, as it is remedial rather than proactive in nature. With the advent of ML, companies in sectors that rely on machines—such as logistics, transport, manufacturing, and so on—can make use of it to accurately gauge patterns hidden in the performance data of their machinery. Known as predictive maintenance, this approach helps reduce the risk of unexpected failures and helps eliminate unforeseen expenses and downtime. Solutions like IBM Watson ML leverage historical data, flexible analysis, workflow visualization, and feedback loops to ensure machines are administered the necessary maintenance before they malfunction.
One of the earliest applications of machine learning software has been in the detection of spam. Traditionally, email service providers rely on rule-based filters to fight spam. However, solutions from companies like Google can be used to create ML-infused spam filters that independently generate and implement new rules through the use of neural networks.
Unsupervised learning is a core feature of advanced ML tools, such as those from SAS Institute. This feature allows ML to be used in the development of product recommendation systems. Numerous e-commerce websites make use of ML to provide product recommendations to customers. ML algorithms analyze the purchase history of a customer to identify patterns and process the gleaned insights to suggest similar products.
Machine learning solutions like Microsoft Azure ML thrive on large volumes of historical data. In the finance segment, which is notorious for the constant generation of intense quantities of data, ML tools can be used in portfolio management, customer service, financial analysis, customer security, loan underwriting, algorithmic trading, chatbots, sentiment analysis, and fraud detection.
Image recognition, also referred to as computer vision, is one of the most interesting capabilities of ML software. Solutions like Amazon
Machine Learning has the ability to derive symbolic and numeric information from images. This data can then be combined with processes such as data mining, database knowledge discovery, and pattern recognition to swiftly and accurately recognize images. This feature has applications in numerous industries, including healthcare, retail, and automobile.
Machine learning is leveraged in medical diagnosis through advanced solutions such as SAP ML. In this segment, ML software is used to reduce healthcare costs, improve patient health, generate effective treatment plans, boost diagnostics, and reduce hospital and insurance bureaucracy. Machine learning in healthcare enables near-perfect diagnoses, prediction of re-admissions, accurate medicine recommendations, and identification of high-risk patients.
Customer Lifetime Value Prediction
Marketing departments place a premium on the accurate derivation of metrics such as customer segmentation and customer lifetime value prediction. Most companies across industries have access to large volumes of data, and ML solutions like Baidu PaddlePaddle can effectively derive actionable business insights from them. ML boosts the marketing capabilities of companies across the globe by enabling them to predict customer behavior and purchasing patterns as well as by helping them send individualized offers to customers based on the principles of targeted marketing.
Machine learning is the foundation for numerous other cutting-edge solutions that are being used in the business world today. As more and more industries adopt ML, its applications will increase manifold, ultimately helping users boost profitability and maximize customer satisfaction.