Building Generative AI Apps on Amazon Web Services — My First Experience
Large companies aren’t entirely sure what to do with generative AI, but they want to do something. Some are exploring the technology with internal hackathons. As an engineer and data scientist at one of Australia’s ‘big four’ banks, I’ve been roped into three of these exciting events over the past month alone.
They serve as a great way for the firm’s knowledge workers — both technical and non-technical — to brainstorm generative AI use cases, test drive the AI tool stack that’s available in the market, and quickly cook up some working prototypes for decision-makers to review.
It’s not every day that your firm pays you to explore the latest tools in enterprise generative AI.
In this article, I wanted to share my experiences playing with the AI stack from Amazon Web Services (AWS) to my fellow analysts, engineers and data scientists. The kind folks at AWS provided us hackathon participants access to: Generative AI requires large datasets and massive computing power to train huge neural networks on that data, which makes the public cloud an ideal platform choice. Major public cloud providers like Amazon, Google, and Microsoft are locked in a…
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