Providing a significant competitive edge for many companies, machine learning is a type of artificial intelligence that uses historical data to predict outcomes more accurately. Common use cases include recommendation engines, malware threat detection and business process automation.

By correlating data with behaviors over time, machine learning algorithms can surface associations and help teams align their products and marketing with customer preferences. It is a key driver for ride-share businesses and many advertisers, for example.

However, it is not perfect. Machine learning bias can be an issue, with algorithms trained on poor datasets producing inaccurate results. If important populations are excluded from the dataset or the data contains errors, the ML models produced will deliver flawed results, and a business that bases key processes on such biased models can suffer reputational damage – and even regulatory sanctions.

Major vendors such as Amazon and Google offer a range of machine learning platform services covering everything from data collection and preparation to model building, training and application deployment.

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