Executive Briefing: Unpacking AutoML

AutoML is one of the hot topics at the forefront of AI research in academia as well as R&D work in industry. Nearly all of the public cloud vendors promote some form of AutoML service. Tech unicorn companies such as Uber have also been developing AutoML services for their data platforms, which are migrating into open source. Meanwhile a flurry of tech startups promise to democratize machine learning for enterprise customers. Ostensibly, automated machine learning will help put ML capabilities into the hands of nonexperts, help improve the efficiency of ML workflows, and accelerate AI research overall. While in the long term AutoML services promise to automate the end-to-end process of applying ML in real-world business use cases, there are still questions about the capabilities and limitations in the near term and if there are business risks involved.

Paco Nathan outlines the history and landscape for vendors, open source projects, and research efforts related to AutoML. Starting from the perspective of an AI expert practitioner who speaks business fluently, Paco unpacks the ground truth of AutoML—translating from the hype into business concerns and practices in a vendor-neutral way. You’ll take a look at where the boundaries are emerging between what we call machine learning and what we call artificial intelligence—all jokes about PowerPoint aside. Then you’ll look toward near-term future scenarios: What considerations a business leader should be making in the industry today to prepare for the on-the-ground realities tomorrow.