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.
AWS offers a broad array of services and infrastructure at all three layers of the machine learning technology stack. More than 100,000 customers trust AWS for Machine Learning, and the company aims to put ML in the hands of every developer, data scientist, and enthusiast with newly announced offerings like Amazon CodeWhisperer, a new ML-powered pair programmer that helps developers improve productivity by reducing the time to build software applications significantly.
To learn more about AWS machine learning services, and how they’re helping customers accelerate their machine learning journeys, visit stage.twimlai.net/go/awsml.
And also be sure to check out stage.twimlai.net/go/sagemaker to sign up for SageMaker Studio Lab.