Source:- insights.dice.com
For years, the sheer messiness of data slowed efforts to launch artificial intelligence (A.I.) and machine learning projects. Companies weren’t willing to wait a year or two while data analysts cleaned up a massive dataset, and executives sometimes had a hard time trusting the outputs of a platform or tool built on messy data.
Data pre-processing is a well-established art, and there are many tech pros out there who specialize in tweaking datasets for maximum validity, accuracy, and completeness. It’s a tough job, and someone has to do it (usually with the assistance of tools, as well as specialized libraries such as Pandas). But now IBM is trying to apply A.I. to this issue, via new data prep tools within AutoAI, itself a tool within the cloud-based Watson Studio.
“We have seen that complexity of data infrastructures can be daunting to the most sophisticated companies, but it can be overwhelming for those with little to no technical resources,” Rob Thomas, General Manager of IBM Data and AI, wrote in a statement.“The automation capabilities we’re putting Watson Studio are designed to smooth the process and help clients start building ML models and experiments faster.”
In addition to data cleanup, AutoAI includes a number of other tools for building A.I. and ML algorithms, including ones that set optimal hyperparameters (which are the parameters with values set before the machine’s learning begins). There’s also IBM Neural Networks Synthesis, or NeuNetS, which creates customized neural networks (users are asked to optimize for either speed or accuracy).
IBM is competing fiercely with Google (which is plunging into the ML-automation game with AutoML Video and AutoML Tables, with other tools surely on the way) and Microsoft (which has automation and recommendation tools built into its Azure Machine Learningplatform) to claim the attention of companies interested in the A.I./ML market. If that wasn’t enough of a crowded landscape, Amazon is plunging heavily into the enterprise-automation game with Amazon Personalize, which streamlines everything from mobile-app development to email marketing.
Of course, the rise of A.I./ML automation could lead to a new host of problems. Sure, having tech professionals build bespoke algorithms and tools in-house is a painstaking process with a fair amount of risk (if you fail, you’ve burned tons of time and resources), but there’s the reasonable expectation that you’ll have something tailored to your needs, based on reliable data and math. When you begin to automate these processes, you risk obfuscating at least a portion of the data and logic behind dashboards—which might lead some to question the output of the work.
Then again, many firms can’t afford to even begin an internal, customized A.I./ML program; in that context, these automated solutions are the best (and perhaps only) bet if they want to get into this particular game.
For tech professionals, these new tools are yet another sign that the A.I./ML market is maturing. Those professionals who understand how these tools work—as well as the underlying logic and theories—will have their pick of positions, as companies desperate for A.I./ML talent are willing to pay enormous salaries and benefits. Although these technologies might seem daunting, there are a number of resources designed to give you a solid education; check them out.
