Machine learning has the potential to transform the way organisations interact with the world, to move faster and to provide better customer experience. But while machine learning’s long-term potential certainly looks bright, its adoption in the enterprise may advance more slowly than originally thought. So what’s the holdup? John Rakowski, Market Specialist for Application Performance Management and Analytics, at AppDynamics discusses the challenges for enterprises when adopting machine learning technology.
Part of the challenge is a lack of understanding around what machine learning is. Machine learning is an application or subset of AI, which is generally thought of as higher-order decision-making intelligence. Machine learning is really about applying mathematics to different domains. It locates meaning within extremely large volumes of data by cancelling out the noise. It uses algorithms to parse the data and draw conclusions from it, such as what constitutes normal behaviour.
It’s important to understand that machine learning algorithms don’t enter chess tournaments. What they are really good at is adapting to changing systems without human intervention while continuing to differentiate between expected and anomalous behaviour. This makes machine learning useful in all kinds of applications - think everything from security to healthcare -as well as classification and recommendation engines, and voice and image identification systems.
Consumers interact daily with dozens of machine learning systems including Google Search, Google ads, Facebook ads, Siri and Alexa, as well as virtually any online product recommendation engine from Amazon to Netflix. The challenge for enterprises is understanding how machine learning can add value to their business.
Machine learning is usually introduced into an enterprise in one of two ways. The first is that one or two employees start applying machine learning to gain insight into data they already have access to. This requires a certain amount of expertise in data science and more importantly, domain knowledge. An understanding of the business value and the customer need for digital services (applications) that are utilised is fundamental— but these skills are often in short supply.
The second is by purchasing a solution, such as security software or application performance monitoring solution, that uses machine learning. This is by far the easiest way to begin to realise the benefits of machine learning.
