Posted On: Dec 8, 2020

Amazon SageMaker JumpStart helps you easily and quickly bring machine learning (ML) applications to market using pre-built solutions for common use cases and open source models from popular model zoos.

Today, many ML developers find it difficult to get started with machine learning. These ML developers want to get models up and running and integrate them into solutions to solve their business problems. However, the process of building, training, and deploying a model and stringing together different components can take months or longer for experienced practitioners or ML developers new to machine learning.

To make it easier to get started, Amazon SageMaker JumpStart provides a set of solutions for the most common use cases, such as fraud detection, predictive maintenance, and demand forecasting, that can be deployed readily with just a few clicks. The solutions are fully customizable and showcase the use of AWS CloudFormation templates and reference architectures so you can accelerate your ML journey. Amazon SageMaker JumpStart also provides one-click deployment and fine-tuning of more than 150 pre-trained models from popular model zoos, including PyTorch Hub and TensorFlow Hub. One-click deployment and fine-tuning features are available for natural language processing, object detection, and image classification models, so you can minimize the time to deploy open source models for your own use case.

Starting today, Amazon SageMaker JumpStart is now generally available in all regions where Amazon SageMaker Studio is available. To get started with Amazon SageMaker JumpStart, read the blog or refer to the documentation.