This full connected has it all: news, updates on AI/ML tooling, discussions about AI workflow, and learning resources. Chris and Daniel breakdown the various roles to be played in AI development including scoping out a solution, finding AI value, experimentation, and more technical engineering tasks. They also point out some good resources for exploring bias in your data/model and monitoring for fairness.

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Featuring:

• Chris Benson – Website, GitHub, LinkedIn, X
• Daniel Whitenack – Website, GitHub, X

Show Notes:

Streamlit:

• Streamlit funding announcement
• Previous Practical AI episode about Streamlit

GPU acceleration in Windows Subsystem for Linux

Fairness and bias:

• Google’s explanation of bias in their ML crash course
• IBM fairness 360
• Google’s responsible AI practices
• Driven Data’s Deon project
• Previous Practical AI episode about bias in AI and hiring
• US Department of Defense Ethical principles for AI

Chris’s personal, COVID-related blog post

Something missing or broken? PRs welcome!