STAC AI Working Group
STAC uses the term "artificial intelligence" (AI) as an umbrella term for machine learning, deep learning (and other neural approaches), and any other techniques for getting computers to do what only humans could do a few years ago. (We're not trying to get into philosophical debates, but we need a vocabulary.)
The use of AI 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 AI is both blessed and cursed by an enormous variety of techniques and technologies, including AI 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 AI workflows.
The STAC AI Working Group develops and promotes benchmark standards for key AI workloads in finance, which enables customers, vendors, and STAC to make apples-to-apples comparisons of techniques and technologies.
Benchmark prototypes have been developed for deep financial time series and natural language processing, as well as real-time inference of signals from market data. (STAC members can access presentations at the foregoing links.)
To influence these and other emerging standards in AI benchmarking, please click on the "Enable me!" button to the right.
STAC AI Reports
Learning and running this benchmark suite
Get access to this domain
If you'd like to obtain privileged materials from this domain, or if you would like to participate in this group, please click the button below.