Amazon SageMaker JumpStart introduces new multimodal (long-form text, tabular) financial analysis tools

Amazon SageMaker JumpStart helps you quickly and easily get started with machine learning. SageMaker JumpStart provides a set of solutions for the most common use cases that can be deployed readily with just a few clicks and one-click deployment and fine-tuning of popular open source models. Starting today, you can now access a collection of multimodal financial text analysis tools, including example notebooks, text models, and a solution.

With this new release, you can use the new set of multimodal financial analysis tools within Amazon SageMaker JumpStart. With these new tools, you can enhance your tabular ML workflows with new insights from financial text documents and potentially help save up to weeks of development time. Using the new SageMaker JumpStart Industry SDK, you can easily retrieve common public financial documents, including SEC filings, and further process financial text documents with features such as summarization and scoring for sentiment, litigiousness, risk, readability etc. In addition, you can access pre-trained language models trained on financial text for transfer learning, and use example notebooks for data retrieval, text feature engineering, multimodal classification and regression models. Lastly, you can access a solution for corporate credit scoring, which is fully customizable and showcases the use of AWS CloudFormation templates and reference architectures so you can accelerate your machine learning journey.

Amazon SageMaker JumpStart is available in all regions where Amazon SageMaker Studio is available. To learn more about the new multimodal financial text analysis tools, view these two blogs: "Use SEC text for ratings classification using multimodal ML in Amazon SageMaker JumpStart" and "Use pre-trained financial language models for transfer learning in Amazon SageMaker JumpStart." To get started with SageMaker JumpStart, refer to the documentation.