IBM’s AutoAI and the Race to Automate ML and A.I. Posted on : Jun 13 - 2019

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 Learning platform) to claim the attention of companies interested in the A.I./ML market. View More