From the course: Implementing a Data Strategy for Responsible AI
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Real-time response systems
From the course: Implementing a Data Strategy for Responsible AI
Real-time response systems
- [Instructor] Realtime interaction with deployed generative AI models has the world's leading companies in a frenzy. They want a live service that responds to user inquiries instantly. You've engaged with these services, like the live customer service chatbot or the live product recommendation engine. Their purpose is to increase employees' productivity by reducing the human hours dedicated to answering the same set of questions from customers. But here's the thing: these types of models have additional requirements that make them tougher to build and easier to break. These deployed generative AI models are continuously ingesting fresh data from various sources. This means that they're trying to quickly interpret the current context of customer requests. If you're tasked with building this type of model, then pay special attention to two computational aspects. That's model optimization and low latency processing. You want to implement model optimization methods that'll try to reduce…
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