But Lambda allows you to continually improve the logic and respond to changes in that data using pretty detailed rules across the content of the messages. Data streams and IoT are making a lot of use of data in real time, and tools like Kenesis and Lambda are great at managing all that data.
And then a third bucket is: You want to know ‘what will happens in the future?’ That’s where Amazon Machine Learning plays a role. It allows you to provide very low-latency, high-load, real-time predictions on models you’ve already trained. Lambda can be integrated to execute those predictions based on the Machine Learning API, then send that data back to your apps.
Google’s cloud has been making a big deal recently about its machine learning, big data and cognitive computing functionality. The company recently developed a program that beat highly-ranked players at the ancient game of Go, for example. What would you say to people who see Google as a more savvy big data, machine learning company compared to Amazon and AWS?
If you look at one of the very early screenshots of the Amazon.com gateway page from around 1996, there were just books available but a million titles. But even in 1996 you would have seen a feature called ‘Eyes and Editors’ which was an early foray into using machine learning to help customers navigate a large and growing catalog of products on Amazon retail. That was very early on and of course since then we’ve been using machine learning and artificial intelligence across the company.
Everything from recommendations – people who read this also read this; people who bought this also bought this – to helping guide customers through the very broad catalog that we have and using it for fulfillment. We do a lot of work with fraud detection and prevention; we sponsor two professorships in machine learning at the University of Washington. In addition to all of that, we took all of that internal knowledge and technology and exposed it to customers through AWS with Amazon Machine Learning.
I’m pretty comfortable with the credentials we’ve built up in Machine Learning. We apply those relentlessly across the company for the benefit of customers in helping search, identify and discover, and then helping developers apply those exact same algorithms to the data sets they already have on AWS, allowing them to build out both batch and real-time predictions for their applications. I think our credentials are pretty well established.
One of Amazon’s chief competitors, Microsoft, emphasizes its capabilities in hybrid cloud computing as a strategic advantage compared to AWS. Microsoft has plans to offer a product named Azure Stack, which gives customers an infrastructure stack to run in their own data centers that mirrors the Azure public cloud. Correct me if I’m wrong but AWS doesn’t really have something like that. Is the lack of an on-premises AWS cloud holding the company back from a portion of the market that wants something like that?
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