Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. Sagemaker provides a number of machine learning algorithms ready to be used for solving a number of tasks. Here I have used a Sagemaker P type instance in this project.

• Python Programming
• Amazon AWS account

To train and deploy an image classifier using Amazon Sagemaker. Here I have used the image classification algorithm from Sagemaker to create, train and deploy a model that will be able to classify 37 breeds of dogs and cats from the popular IIIT-Oxford Pets Dataset.

To train and deploy a Semantic Segmentation model using Amazon Sagemaker. Here I have used the semantic segmentation algorithm from Sagemaker to create, train and deploy a model that will be able to segment images of dogs and cats from the popular IIIT-Oxford Pets Dataset into 3 unique pixel values. That is, each pixel of an input image would be classified as either foreground (pet), background (not a pet), or unclassified (transition between foreground and background).

To train and deploy an object detector using Amazon Sagemaker. Here I have used the SSD Object Detection algorithm from Sagemaker to create, train and deploy a model that will be able to localize faces of dogs and cats from the popular IIIT-Oxford Pets Dataset.

The AWS account will be charged as per your usage. Please make sure that you are able to access Sagemaker within your AWS account. If your AWS account is new, you may need to ask AWS support for request access to certain resources.