Emily Webber

Neuron empowers AI scientists and researchers to invent new model architectures, build the next generation of foundation models on AWS Trainium, and push the boundaries of generative AI research and open source innovation.

Train machine learning (ML) models faster on Amazon SageMaker with state-of-the-art training tools and the highest-performing ML compute infrastructure currently available. SageMaker interactive debugging and profiling tools can uncover complex model behaviors and dissect hardware utilization in near real time.

I’ve noticed an increasingly worrying trend in the industry of specialisation and silos at the expense of collaboration, shared responsibility and valuable outcomes.

Training ML models at scale often requires significant investments. In this session, learn how Amazon SageMaker reduces the time and cost to train and tune large-scale ML models without the need to manage infrastructure.

Join this session to learn how Amazon SageMaker Studio Lab is accelerating machine learning (ML) education with a free, no-configuration ML environment and no AWS account required to get started. Amazon SageMaker Studio Lab offers an open-source Jupyter notebook environment integrated with the GitHub software development platform and preconfigured with the most popular ML tools, frameworks,...

Today, AWS customers use Amazon SageMaker to train and tune millions of machine learning (ML) models with billions of parameters. In this session, learn about advanced SageMaker capabilities that can help you manage large-scale model training and tuning, such as distributed training, automatic model tuning, optimizations for deep learning algorithms, debugging, profiling, and model...

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). In this session, see how easy it is to train and tune models in SageMaker Studio.

In this workshop, we explore the new AWS Predictive Maintenance Using Machine Learning solution. This solution deploys a machine learning model and an example dataset of turbofan degradation simulation data to train the model to recognize potential equipment failures.

Across a wide spectrum of industries, customers are starting to utilize prediction maintenance models to proactively fix problems before they impact production. The result is an optimized supply chain and improved working conditions.