Table of Contents
- Market Maturity and the Vendor Ecosystem
- Considerations for Using AutoML Platforms
- Vendor Review
- Near-Term Outlook
- Key Takeaways
- About Andrew Brust
The world of Artificial Intelligence (AI) is at a crossroads between potential and accessibility. AI is powerful, and far more actionable than it has been in the multiple decades since it emerged as a discipline, and yet, the amount of trial and error, hunch, and bespoke effort involved in doing rigorous AI work is still significant. Of course, this is the case with many new technologies as they cross the chasm between what they can do theoretically and what they are able to do practically and efficiently.
The game changer in this apparent quagmire is automated machine learning (increasingly known simply as AutoML). AutoML is busting the monopoly that highly-trained data scientists have over profitable and advantageous use of AI, because it enables non-specialists to work through the bits of AI that were previously off-limits. It’s a win-win though, as AutoML also helps those data scientists work at a higher level and get more of their specialized work done.
Labor of Love
After selection of a data set and its target/label column, manual AI (if we may use that as shorthand for conventional, work with machine learning and deep learning, without use of AutoML) involves several steps:
- Complex data preprocessing, including steps called feature extraction and feature engineering (features are the columns in a data set whose input values are germane to the model’s predictions).
- Selection of one or more algorithms with which to build a model or set of models.
- Setting values for the algorithms’ so-called hyperparameters.
- Training the model.
- Testing the model.
There is more, too, including model selection, deployment of the model for production use, hosting the model (complete with the generation of a Web services REST API interface), and post-production management of the model, including retraining it if/as patterns in the data change and the model’s accuracy degrades. This ML workflow is illustrated in Figure 1.
AutoML platforms automate various subsets of the above steps and the technology is already making AI accessible to non-specialists. That is as it should be for any technology that seeks to become mainstream. Lots of people drive without being mechanics. Likewise, information workers who are well-acquainted with their data, and have strong motivations to build predictive models around it, should be able to so without the assistance of a specialist. However, it is still early days for the technology and looking at each AutoML solution creates the impression that vendors are still getting their bearings in this market, as are users. Things are still gelling and in their formative stages. Coming up to speed on AutoML in the pioneering days has its advantages though; it lets us get a leg up before the technology is ubiquitous.
This report provides an overview of AutoML solutions spanning the open source and commercial worlds, the major public cloud providers and vendors of software that can run either on-premises or in the cloud, application programming interface (API)- and command line interface (CLI)-level solutions, as well as products with full-on user interfaces (UIs); and solutions aimed at business users, data scientists, or both.