AutoML Is Democratizing And Improving AI

There's an irony around Artificial Intelligence (AI) work: it involves a lot of manual, trial and error effort to build predictive models with the highest accuracy. With a seemingly continuous emergence of machine learning and deep learning frameworks, and updates to them, as well as changes to tooling platforms, it's no wonder that so much AI work is so ad hoc. But still, why would a technology that's all about automation involve so much bespoke effort?

The problem with all the manual work is twofold: first, it makes it almost impossible for people without training in data science to do AI work; and second, people with data science backgrounds themselves face a very inefficient workflow.


That logjam is starting to clear now, though, with the emergence of automated machine learning (AutoML). A few companies, like DataRobot, specialize in it. Other AI startups, like Dataiku, H20, and RapidMiner, and established enterprise software companies like Tibco, have broad AI platforms that feature AutoML capabilities too. So do the major public cloud platforms, including Microsoft Azure, Amazon Web Services and Google Cloud Platform. There are open source AutoML frameworks as well, like Auto-sklearn, Auto-Keras and Uber's recently open-sourced Ludwig platform. READ MORE ON: ZDNET