In this engaging discussion, Shruti Koparkar, a Senior Product Manager at AWS with expertise in accelerating computing for AI/ML, shares her insights on generative AI. She unpacks how AWS supports businesses, helping them to innovate without getting locked into solutions that don't work. Shruti explains the power of NVIDIA GPUs in fine-tuning AI models and highlights real-world examples like Adobe's utilization of AWS. The conversation emphasizes starting small with AI and how AWS is prepared for the burgeoning future of generative technologies.
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Choosing the Right Model
Choose the right foundation model for your specific generative AI use case.
Consider Amazon Bedrock, which offers diverse models accessible through a single API.
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Data Differentiation
Differentiate your generative AI applications by customizing them with your own data.
Services like Amazon Bedrock and SageMaker allow for secure customization.
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Ready-to-Use Applications
Explore ready-to-use generative AI applications for increased productivity.
Consider tools like Amazon CodeWhisperer for coding assistance.
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If you're a fast-growing company needing to leverage all that Generative AI has to offer, the last thing you want is a vendor lock-in. Or, finding out the AI solution you thought you needed doesn't actually have the juice you need. One solution? Amazon Web Services (AWS). We sit down and talk all things AI models with Shruti Koparkar, Product Marketing Lead, AI/ML Acceleration at AWS.
Timestamps: 01:20 About Shruti Koparkar and AWS 02:50 More on AWS and Generative AI 09:54 X-rated computing used to fine-tune models. 12:46 Adobe focused on AI tools, using AWS. 17:50 AI evolution and AWS preparedness for influx. 21:01 Experienced ARM engineer excited about NVIDIA's potential. 24:08 Identify use cases for small-scale applications.
Topics Covered in This Episode: 1. AWS and its Role in Generative AI 2. AWS and Foundation Models 2. AWS's Involvement with Companies Implementing Generative AI 4. Future Preparations of AWS for Generative AI Developments
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