Continuing Datamation's series on big data, Internet of Things (IoT) and artificial intelligence offerings from major cloud providers, it's time to switch gears from Microsoft Azure to Google Cloud Platform. And given the vast amounts of data that powers the search giant's services, it's only fitting to start with big data and analytics.
For the past several years, our goal at Google has been to play a critical role in bringing the benefits of AI to everyone. Machine learning is at the heart of that goal. In the area of life sciences -- or more specifically, the field of genomics -- we're using ML to derive insights from the human genome. Additionally, due to the scale of human genomic data, we need new techniques to process the datasets using machine learning and cloud computing. We'll examine an analytical technique for verifying the ancestry of a human DNA sample, and show you how to implement it as a system using Google Cloud Platform, TensorFlow and Google Cloud Machine Learning Engine.
In San Francisco this week at Pier 48, overlooking the Giants' AT&T Ballpark, Google Cloud Platform (GCP) executives are holding a user conference to introduce products and services they hope will help make the case for choosing Google in the cloud. Sam Charrington, a cloud and big data analyst and advisor, summed up Google executives' pitch best this week on Twitter: "GCP exec team's operating thesis: 'Cloud's not done. MORE AT NETWORK WORLD: Is Google pushing the cloud envelope too far? Google is seen by many as being behind Amazon Web Services, Microsoft Azure and even IBM in the IaaS cloud market. In a new research note, Deutsche Bank investment analysts predicted that GCP is on a 400 million revenue run rate, which is roughly 20 times less than AWS's.
Google's AI Platform, a cloud-hosted service facilitating machine learning and data science workflows, today gained a new feature in backend models that tap powerful Nvidia graphics chips. In related news, Google debuted a refreshed model training experience that allows users to run a training script on any range of hardware. For the uninitiated, AI Platform enables developers to prep, build, run, and share machine learning models quickly and easily in the cloud. Using built-in data labeling services, they're able to annotate model training images, videos, audio, and text corpora by applying classification, object detection, and entity extraction. A managed Jupyter Notebook service provides support for a slew of machine learning frameworks, including Google's TensorFlow, while a dashboard within the Google Cloud Platform console exposes controls for managing, experimenting with, and deploying models in the cloud or on-premises.
But, the barriers to entry for AI are high – as is cost. In Making Up the Mind, the neuroscientist Chris Frith describes how our perception of the world is not direct, but instead relies on "unconscious reasoning". Before we can perceive an object, the brain must infer what the object is based on the information that reaches our senses. And this constitutes humans' most important ability – the ability to predict and handle unexpected events. As part of this process, sight is the fastest and most accurate channel for information acquisition – we capture 80 percent of our information about the outside world through the eyes.