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Using a Hyperscaler in an Edge AI Project? Read This First

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So you want to build an AI system. Where do you start?

Take multimodal sentiment analysis, for example, which relies on multiple natural language processing and/or computer vision models that are refined through rigorous data labeling and training. Implementing it in, say, a retail customer service kiosk requires infrastructure like databases and visualization tools, web app development environments, deployment and delivery services, and, of course, an AI model training framework or two.

If this is your first foray into AI-enabled system creation, your organization probably doesn’t have all these tools configured in a way that’s conducive to fast AI system prototyping—if it has all the necessary components at all. In these instances, AI engineering hopefuls often turn to hyperscaler cloud platforms like Microsoft Azure, AWS, and Google Cloud. In addition to essentially infinite data capacity and infrastructure services, many of these hyperscalers support end-to-end AI development out of the box or offer API-based integration for specific third-party tools in a few clicks.

Download Using a Hyperscaler in an Edge AI Project? Read This First Whitepaper




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