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Author: arunprsh

README.md

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 Amazon SageMaker Multi-Model Endpoints provides a scalable and cost-effective way to deploy large numbers of custom machine learning models. SageMaker Multi-Model endpoints will let you deploy multiple ML models on a single endpoint and serve them using a single serving container. Your application simply needs to include an API call with the target model to this endpoint to achieve low latency, high throughput inference. Instead of paying for a separate endpoint for every single model, you can host many models for the price of a single endpoint. For detailed information about multi-model endpoints, see Save on inference costs by using Amazon SageMaker multi-model endpoints.
 
-In this repository, we demonstrate how to host two computer vision models trained using the TensorFlow framework under one SageMaker multi-model endpoint.  For the first model, we train a smaller version of AlexNet CNN to classify images from the CIFAR-10 dataset. For the second model, we use a pretrained VGG16 CNN model pretrained on the ImageNet dataset and fine-tuned on the Sign Language Digits Dataset to classify hand symbol images. 
+In this repository, we demonstrate how to host two computer vision models trained using the TensorFlow framework under one SageMaker multi-model endpoint. For a detailed full walkthrough of the example covered in this repo, see this accompanying [AWS blog post](https://aws.amazon.com/blogs/machine-learning/host-multiple-tensorflow-computer-vision-models-using-amazon-sagemaker-multi-model-endpoints/). For the first model, we train a smaller version of AlexNet CNN to classify images from the CIFAR-10 dataset. For the second model, we use a pretrained VGG16 CNN model pretrained on the ImageNet dataset and fine-tuned on the Sign Language Digits Dataset to classify hand symbol images. 
 
 <img src="/img/sagemaker-design-patterns-mme-cv.jpg" alt="SageMaker Multi-model Endpoint"/>