Amazon SageMaker AI JumpStart Industry: Financial

Use SageMaker AI JumpStart Industry: Financial solutions, models, and example notebooks to learn about SageMaker AI features and capabilities through curated one-step solutions and example notebooks of industry-focused machine learning (ML) problems. The notebooks also walk through how to use the SageMaker JumpStart Industry Python SDK to enhance industry text data and fine-tune pretrained models.

Amazon SageMaker AI JumpStart Industry Python SDK

SageMaker Runtime JumpStart provides processing tools for curating industry datasets and fine-tuning pretrained models through its client library called SageMaker JumpStart Industry Python SDK. For detailed API documentation of the SDK, and to learn more about processing and enhancing industry text datasets for improving the performance of state-of-the-art models on SageMaker JumpStart, see the SageMaker JumpStart Industry Python SDK open source documentation.

Amazon SageMaker AI JumpStart Industry: Financial Solution

SageMaker AI JumpStart Industry: Financial provides the following solution notebooks:

This SageMaker AI JumpStart Industry: Financial solution provides a template for a text-enhanced corporate credit rating model. It shows how to take a model based on numeric features (in this case, Altman's famous 5 financial ratios) combined with texts from SEC filings to achieve an improvement in the prediction of credit ratings. In addition to the 5 Altman ratios, you can add more variables as needed or set custom variables. This solution notebook shows how SageMaker JumpStart Industry Python SDK helps process Natural Language Processing (NLP) scoring of texts from SEC filings. Furthermore, the solution demonstrates how to train a model using the enhanced dataset to achieve a best-in-class model, deploy the model to a SageMaker AI endpoint for production, and receive improved predictions in real time.

Credit ratings are traditionally generated using models that use financial statement data and market data, which is tabular only (numeric and categorical). This solution constructs a network of firms using SEC filingsand shows how to use the network of firm relationships with tabular data to generate accurate rating predictions. This solution demonstrates a methodology to use data on firm linkages to extend the traditionally tabular-based credit scoring models, which have been used by the ratings industry for decades, to the class of machine learning models on networks.

You can find these financial services solutions through the SageMaker JumpStart page in Studio Classic.

Amazon SageMaker AI JumpStart Industry: Financial Models

SageMaker AI JumpStart Industry: Financial provides the following pretrained Robustly Optimized BERT approach (RoBERTa) models:

The RoBERTa-SEC-Base and RoBERTa-SEC-Large models are the text embedding models based on GluonNLP's RoBERTa model and pretrained on S&P 500 SEC 10-K/10-Q reports of the decade of the 2010's (from 2010 to 2019). In addition to these, SageMaker AI JumpStart Industry: Financial provides two more RoBERTa variations, RoBERTa-SEC-WIKI-Base and RoBERTa-SEC-WIKI-Large, which are pretrained on the SEC filings and common texts of Wikipedia.

You can find these models in SageMaker JumpStart by navigating to the Text Models node, choosing Explore All Text Models, and then filtering for the ML Task Text Embedding. You can access any corresponding notebooks after selecting the model of your choice. The paired notebooks will walk you through how the pretrained models can be fine-tuned for specific classification tasks on multimodal datasets, which are enhanced by the SageMaker JumpStart Industry Python SDK.

The following screenshot shows the pretrained model cards provided through the SageMaker AI JumpStart page on Studio Classic.

Amazon SageMaker AI JumpStart Industry: Financial Example Notebooks

SageMaker AI JumpStart Industry: Financial provides the following example notebooks to demonstrate solutions to industry-focused ML problems:

To preview the content of the example notebooks, see Tutorials – Finance in the SageMaker JumpStart Industry Python SDK documentation.

Amazon SageMaker AI JumpStart Industry: Financial Blog Posts

For thorough applications of using SageMaker AI JumpStart Industry: Financial solutions, models, examples, and the SDK, see the following blog posts:

Amazon SageMaker AI JumpStart Industry: Financial Related Research

For research related to SageMaker AI JumpStart Industry: Financial solutions, see the following papers:

Amazon SageMaker AI JumpStart Industry: Financial Additional Resources

For additional documentation and tutorials, see the following resources: