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Today we're joined by Alex Smola, Vice President and Distinguished Scientist at AWS AI.
We had the pleasure to catch up with Alex prior to the upcoming AWS Machine Learning Summit, and we covered a TON of ground in the conversation. We start by focusing on his research in the domain of deep learning on graphs, including a few examples showcasing its function, and an interesting discussion around the relationship between large language models and graphs. Next up, we discuss their focus on AutoML research and how it's the key to lowering the barrier of entry for machine learning research.
Alex also shares a bit about his work on causality and causal modeling, introducing us to the concept of Granger causality. Finally, we talk about the aforementioned ML Summit, it's exponential growth since its inception a few years ago, and what speakers he's most excited about hearing from.
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.
