Extract of article by David Linthicum
Machine learning, an approach and set of technologies that use AI concepts, is directly related to pattern recognition and computational learning. It’s an old concept, first defined in 1959 as giving computers the capacity to learn without reprogramming.
Machine learning was once out of the reach of most enterprise budgets, but today, public cloud providers’ ability to offer machine-learning services makes this technology affordable. I’d like to bring you up to date on machine learning and its relevance to today’s IT development and deployment needs, especially for those working within a cloud environment.
What is machine learning?
Machine learning is really about the study of algorithms that have the ability to learn through patterns and, based on that, make predictions against patterns of data . It’s a better alternative to leveraging static program instructions and instead making data-driven predictions or decisions that will improve over time without human intervention and additional programming.
One of the concerns, as machine learning becomes more affordable through the use of cloud platforms, is that the technology will be misapplied. This already seems to be a pattern, as cloud providers promote machine learning as having wide value. However, that value won’t be realized if machine learning is applied to systems that can’t benefit from making predictions based on patterns found in data.
So what’s the bottom line with machine learning and the cloud? There is actual value there for businesses, if correctly applied. Enterprises looking for applications for this technology may find that, in some cases, machine learning could be a game-changer for the business.