Julien Simon

In this session, we’ll introduce you to Transformer models and what business problems you can solve with them. Then, we’ll show you how you can simplify and accelerate your machine learning projects end-to-end: experimenting, training, optimizing, and deploying.

In this code-level workshop, you’ll learn how to use hyperparameter optimization (HPO) and AutoML on Amazon SageMaker, in order to quickly and easily build highly accurate machine learning models. Using a real-life dataset, you’ll first use HPO to tune models built with the popular XGBoost algorithm.

In this builders session, we show you how to bring an existing Jupyter notebook from your local environment to Amazon SageMaker. Learn how to automate repository and package operations.

TensorFlow is one of several open-source deep learning frameworks used in machine learning development that is currently popular among developers. But it can be challenging to scale TensorFlow model training and inference.

Amazon SageMaker is a fully managed service that offers developers and data scientists the flexibility to build, train, and deploy machine learning models through modular capabilities. In this session, we dive deep into the technical details of each module so you understand how to label and prepare your data, choose an algorithm, train and optimize the model, and make predictions.