Get Started with SageMaker Studio

Note

The following examples have been optimized for SageMaker Studio.

Basic training example for SageMaker Studio

• An Introduction to Linear Learner with MNIST
• Amazon SageMaker Studio Walkthrough

SageMaker-Debugger

• Visualizing Debugging Tensors of MXNet training
• Debugging Amazon SageMaker training jobs in real time with Debugger
• Amazon SageMaker Debugger - Using built-in rule

Framework examples

• Using the Apache MXNet Module API with SageMaker Training and Batch Transformation
• Hosting ONNX models with Amazon Elastic Inference
• Training and Hosting a PyTorch model in Amazon SageMaker
• Training and Serving with TensorFlow on Amazon SageMaker

Model compilation with Neo

• Deploy pre-trained GluonCV SSD Mobilenet model with SageMaker Neo
• Model Optimization with an Image Classification Example
• Deploying pre-trained PyTorch vision models with Amazon SageMaker Neo
• Deploying pre-trained PyTorch VGG19 model with Amazon SageMaker Neo
• Using SageMaker Neo to Compile a Tensorflow U-Net Model
• Customer Churn Prediction with XGBoost

Bring your own container to Studio

• SageMaker Batch Transform using an XgBoost Bring Your Own Container (BYOC)