Deploy Models with SageMaker

Examples on how to host models for predictions, inference, and transformations with SageMaker.

Batch transform

• Get started with Batch Transform
• Apache MXNet
• Data types
• PyTorch
• TensorFlow

Bring your own container

• BYOC
• PyTorch
• Scikit
• TensorFlow

Data types

• Data Types

Model Compilation with Neo

• Get started with model compilation with Neo
• Apache MXNet
• PyTorch
• TensorFlow

Model deployment

• Use Your Own Inference Code with Amazon SageMaker XGBoost Algorithm
• TensorFlow BYOM: Train locally and deploy on SageMaker.
• Bring Your Own Model (k-means)
• Amazon SageMaker XGBoost Bring Your Own Model

Elastic inference

• Using Amazon Elastic Inference with MXNet on Amazon SageMaker
• Using Amazon Elastic Inference with MXNet on an Amazon SageMaker Notebook Instance
• Hosting ONNX models with Amazon Elastic Inference

Endpoints

• Get started with endpoints
• A/B testing
• Multi-model endpoints

Model monitor

• Get started with Model Monitor
• Model Monitor with Tensorflow
• Visualization
• Detect post-training data and model bias

Multi-Model Deployment

• Amazon SageMaker Multi-Model Endpoints using Scikit Learn

Nvidia Triton Inference

• Triton on SageMaker - Deploying a PyTorch Resnet50 model
• Triton on SageMaker - NLP Bert