On the verge of new AI possibilities

In this episode Jaana and Mat are joined by Daniel and Miriah to dive into AI in Go. Why has python historically had a bigger foothold in the AI scene? Is machine learning in Go growing? What libraries and tools are out there for someone looking to get started with AI? And where do you start if you don’t have enough data for your own models?

Discuss on Changelog News

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Featuring

• Daniel Whitenack – Twitter, GitHub, Website
• Miriah Peterson – Twitter, GitHub
• Mat Ryer – Twitter, GitHub, LinkedIn, Website
• Jaana B. Dogan (JBD) – Twitter, GitHub, Website

Notes and Links

• The Practical AI podcast - Our sister podcast with Daniel Whitenack and Chris Benson
• Gopher’s Slack #data-science - This channel is a great place to ask questions and get started with AI in Go.
• Go Num Libraries - Large family of libraries for statistics, etc. Great for AI.
• Gorgonia - Library that helps facilitate machine learning in Go.
• Awesome Machine Learning - The Go section of this repo is helpful for finding other AI and ML libraries in Go.
• Gopher Data - A hub for users and developers of Go data process, analytics, etc.
• spaCy and thinc - Python Deep Learning tools that introduced type checking, suggesting this is a valuable thing in ML.
• Google Cloud AutoML - Google’s machine learning models, which can be a good starting point for many orgs.
• Azure Machine Learning - Microsoft’s machine learning tooling and offering. Also a great place for many orgs to start.
• Packyderm - Data science platform with an open source offering.
• Go West Conference - A Go conference in Utah that our guest Miriah helps organize.

Episode source