Many individuals are creating their own ecommerce and online stores in order to sell their products and services. This simplifies and speeds the process of getting products out to your selected markets. This is a critical key indicator for the success of your business. Artificial Intelligence/Machine Learning (AI/ML) and automation can offer you an improved and seamless process for image manipulation. You can take a picture identifying your products. You can then remove the background in order to publish high-quality and clean product images. These images can be added to your online stores for consumers to view and purchase. This automated process will drastically decrease the manual effort required, though some manual quality review will be necessary. It will increase your time-to-market (TTM) and quickly get your products out to customers. This blog post explains how you can automate the removal of image backgrounds by combining semantic segmentation inferences using Amazon SageMaker JumpStart. You can automate image processing using AWS Lambda. We will walk you through how you can set up an Amazon SageMaker JumpStart semantic segmentation inference endpoint using curated training data. SageMaker JumpStart streamlines the deployment of the prebuilt model on SageMaker, which supports the semantic segmentation algorithm. You can test this using the sample Jupyter notebook available at Extract Image using Semantic Segmentation, which demonstrates how to extract an individual form from the surrounding background. SageMaker JumpStart provides a lot of different semantic segmentation models that are pre-trained with a class of objects it can identify. These models are fine-tuned for a sample dataset. You can tune the model with your dataset to get an effective mask for the class of object you want to retrieve from the image. When you fine-tune a model, you can use the default dataset or choose your own data, which is located in an Amazon S3 bucket. You can customize the hyperparameters of the training job that are used to fine-tune the model. When the fine-tuning process is complete, JumpStart provides information about the model: parent model, training job name, training job Amazon Resource Name (ARN), training time, and output path. We retrieve the deploy_image_uri, deploy_source_uri, and base_model_uri for the pre-trained model. You can host the pre-trained base-model by creating an instance of sagemaker.model.Model and deploying it. In this blog, we review the steps to use Amazon SageMaker JumpStart and AWS Lambda for automation and processing of images. It uses pre-trained machine learning models and inference. The solution ingests the product images, identifies your products, and then removes the image background. After some review and QA, you can then publish your products to your ecommerce store or other medium.