STAC-ML Working Group

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The use of machine learning (ML) to develop models is now commonplace in trading and investment. Whether the business imperative is reducing time to market for new algorithms, improving model quality, or reducing costs, financial firms have to offload major aspects of model development to machines in order to continue competing in the markets.

The field of ML is both blessed and cursed by an enormous variety of techniques and technologies, including ML algorithms, frameworks, libraries, and processor architectures. The options are further increased by machine-learning-as-a-service offerings from all the major cloud providers, as well as countless software and software-as-a-service providers promising to simplify, accelerate, or otherwise enhance ML workflows.

The STAC-ML Working Group develops and promotes benchmark standards for key ML workloads in finance, which enables customers, vendors, and STAC to make apples-to-apples comparisons of techniques and technologies.

The working group has developed benchmark specifications for real-time inference of signals on financial data and is exploring other areas in ML where benchmarks will help with technology discovery and assessment.

To influence these and other emerging standards in AI benchmarking, please click on the "Enable me!" button to the right.

With STAC-ML Markets (Inference), SUTs are comparable or not based on their error. The following pages explain comparability and outline what SUTs are comparable.

STAC-ML Reports


Learning and running this benchmark suite

Other STAC-ML documents

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