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Managed Machine Learning: AI Without the Headaches
Artificial Intelligence and Machine Learning has great potential, but without staff on board with significant skills and training, the barrier to effectively using AI is high and prevents many companies from being successfully using it.
Craig Wiley, the director of product management for Google Cloud’s AI Platform, said that “Machine Learning in the enterprise is in crisis, in my view. As someone who has worked in that space for a number of years, if you look at the Harvard Business Review or analyst reviews, or what have you — every single one of them comes out saying that the vast majority of companies are either investing or are interested in investing in machine learning and are not getting value from it. That has to change. It has to change.”
Cassie Kozyrkov, researcher at Google, compared the benefits of using a Managed Machine Learning platform to that of a Swiss Army knife, and able to provide the user a quicker time to configure, setup, and train, compared to starting from scratch or using existing frameworks.
Bradley Shimmin, chief analyst for AI Platforms, Analytics and Data Management at Omdia, said that “enterprise data science practitioners hoping to put AI to work across the enterprise aren’t looking to wrangle tooling. Rather, they want tooling that can tame the ML lifecycle. Unfortunately, that is no small order. It takes a supportive infrastructure capable of unifying the user experience, plying AI itself as a supportive guide, and putting data at the very heart of the process — all while encouraging the flexible adoption of diverse technologies.”
The benefit of using a pre-configured platform tuned for handling AI problems, according to Andrew Moore, vice president at Google Cloud, is that it can help any company “get serious about moving AI out of pilot purgatory and into full-scale production.”
