4 ways Google Cloud will bring AI, machine learning to the enterprise
Last November, when Google announced that machine learning research luminary Fei-Fei Li, Ph.D. would join Google’s Cloud Group Platform group, a lot was known about her academic work. But Google revealed little about why she was joining the company except she would lead machine learning for the Google Cloud business.
After five months of suspense, yesterday Li revealed the focus of her new role during her keynote address at Google’s cloud developer conference, Cloud Next 2017. She will apply her experience to democratize machine learning to the enterprise. Her task: Study the problems that machine learning could solve in a wide variety of industries and enable enterprises to adopt machine learning.
It sounds more like a job for an enterprise salesman, not a Stanford research professor with over a hundred papers published in the field, but that would be the wrong conclusion. Machine learning has produced amazing results, but its application has been narrow so far, applied to university research and by long-term investors in machine learning research and applications such as Google, Facebook, IBM, and Microsoft to solve their domain-specific problems.
Some of this work is extensible to other industries, such as medical imaging that has produced the same diagnostic accuracy as doctors diagnosing skin cancer and diabetic retinopathy, the leading cause of blindness, mentioned by Li during her keynote. But she is looking for new greenfield applications that enterprises can use.
4 ways Google will enable enterprises to adopt machine learning and AI
Li made four points on the topics of democratizing AI. She began by saying, “Machine learning can deliver, but this remains a field of high barriers. It requires rare expertise and resources that few companies can afford.”
She proposed that Google’s cloud, technology and services serve as an AI and machine learning on-ramp for enterprises.
Because a deep learning algorithm can have tens of millions of parameters, training these machine learning models requires enormous computational resource. Here Li announced the release from beta of the Cloud Machine Learning Engine. This capability is designed for companies with data scientists and machine learning experts who are able to build their own unique machine learning models with libraries such as Tensorflow.
Training big models is computationally intensive and often requires expensive special purpose hardware. Training is iterative, requiring multiple learning cycles to optimize the performance and accuracy of the model. Slow hardware means the model developers have to wait days, weeks or even longer for one training run so that they can iterate to improve the model’s accuracy and performance. A machine learning team’s training resource demand is not consistent with operational systems, resulting in inefficient utilization of the capital investment in on-premise hardware resources.
