Machine Learning

What is Machine Learning?

Machine learning is a subset of artificial intelligence (AI) in which algorithms learn by example from historical data to predict outcomes and uncover patterns not easily spotted by humans. For example, machine learning can reveal customers who are likely to churn, likely fraudulent insurance claims, and more. While machine learning has been around since the 1950s, recent breakthroughs in low-cost compute resources like cloud storage, easier data collection, and the proliferation of data science have made it very much “the next big thing” in business analytics.

To put it simply, the machine learning algorithm learns by example, and then users apply those self-learning algorithms to uncover insights, determine relationships, and make predictions about future trends. Machine learning has practical implications across industry sectors, including healthcare, insurance, energy, marketing, manufacturing, financial technology (fintech), and more. When implemented effectively, machine learning allows businesses to uncover optimal solutions to practical problems, which leads to real, tangible business value.

Why is Machine Learning Important?

While most statistical analysis relies on rule-based decision-making, machine learning excels at tasks that are hard to define with exact step-by-step rules. Machine learning can be applied to numerous business scenarios in which an outcome depends on hundreds of factors — factors that are difficult or impossible for a human to monitor. As a result, businesses use machine learning for predicting loan defaults, understanding factors that lead to customer churn, identifying likely fraudulent transactions, optimizing insurance claims processes, predicting hospital readmission, and many other cases.

Companies that effectively implement machine learning and other AI technologies gain a massive competitive advantage. According to a recent report by McKinsey & Company, AI technologies will create $50 trillion of value by the year 2025. Companies that fail to do the same will be unable to compete with those who embrace the new frontier – and sooner rather than later.

Machine Learning + DataRobot

Historically, machine learning has been a tedious process that requires a lot of manual coding, limiting the ability of organizations to take full advantage of the technology. Without teams of difficult-to-find data scientists at their disposal, companies are limited in the number of models they are able to develop and test – and often those models take so long to develop, they are outdated by the time they are complete.

To solve this problem, DataRobot invented automated machine learning. Building a high-quality machine learning model often involves a combination of elaborate feature engineering, a Ph.D.-level knowledge of statistics, and extensive software engineering experience. DataRobot strives to make machine learning more accessible to everyone in every organization by incorporating the knowledge and best practices of the world’s best data scientists into a fully automated modeling platform that you can use regardless of data science experience or coding knowledge, delivering insights an order of magnitude faster than was previously possible.