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Daniel Whitenack

Applications of Go in data science and AI


A frequent presenter at Go conferences, Daniel Whitenack promotes applications of Go in data science and AI.

Whitenack has published a large set of public repositories relevant to data science: https://github.com/dwhitena/

At GopherCon 2021, Whitenack delivered a workshop Production AI with Go. He demonstrated ways of working with deep learning methods in Go. For example, we can train and test deep learning models with the Python client to TensorFlow/Keras and then export computational graphs for use in Go. The implementation, the production system, can be built with Go, providing security, high performance, scalability, and the possibility of distributing the AI workload across systems. Such are the advantages of being a multilingual data scientist. See Daniel Whitenack’s GitHub repository from Production AI with Go..

Whitenack shows how to build integrated, intelligent web applications, including those that call on generative AI and large language models. See Daniel Whitenack’s GitHub repository for GopherCon 2023 Generative AI Workshop Materials.

Whitenack and co-host Chris Benson offer the Practical AI podcast series, “Making Artificial Intelligence Practical, Productive, and Accessible to Everyone.” Here is a recording of the podcast Large Models on CPUs, a discussion between Whitenack and Mark Kurtz from May 2023:

References #

Whitenack, Daniel. 2017. Machine Learning With Go: Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language. Birmingham, UK: Packt. [ISBN-13: 978-1785882104] GitHub repository. Ignore references to a second edition of this book. It was never published.

Daniel Whitenack’s 2016 GopherCon presentation Go for Data Science has an associated GitHub repository:

Go data-science-focused resources demonstrated by Whitenack:

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