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Today we’re joined by Cody Coleman, co-founder and CEO of Coactive AI. In our conversation with Cody, we discuss how Coactive has leveraged modern data, systems, and machine learning techniques to deliver its multimodal asset platform and visual search tools. Cody shares his expertise in the area of data-centric AI, and we dig into techniques like active learning and core set selection, and how they can drive greater efficiency throughout the machine learning lifecycle. We explore the various ways Coactive uses multimodal embeddings to enable their core visual search experience, and we cover the infrastructure optimizations they’ve implemented in order to scale their systems. We conclude with Cody’s advice for entrepreneurs and engineers building companies around generative AI technologies.
You know AWS as a cloud computing technology leader, but did you realize the company offers a broad array of services and infrastructure at all three layers of the machine learning technology stack? AWS has helped more than 100,000 customers of all sizes and across industries to innovate using ML and AI with industry-leading capabilities and they’re taking the same approach to make it easy, practical, and cost-effective for customers to use generative AI in their businesses. At the bottom layer of the ML stack, they’re making generative AI cost-efficient with Amazon EC2 Inf2 instances powered by AWS Inferentia2 chips. At the middle layer, they’re making generative AI app development easier with Amazon Bedrock, a managed service that makes pre-trained FMs easily accessible via an API. And at the top layer, Amazon CodeWhisperer is generally available now, with support for more than 10 programming languages.
To learn more about AWS ML and AI services, and how they’re helping customers accelerate their machine learning journeys, visit twimlai.com/go/awsml.
