Why is ML is so poorly adopted in small organizations (hint: it’s not because they don’t have enough data)? In this episode, Kirsten Lum from Storytellers shares the patterns she has seen in small orgs that lead to a successful ML practice. We discuss how the job of a ML Engineer/Data Scientist is different in that environment and how end-to-end project management is key to adoption.

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Sponsors:

• The Changelog – Conversations with the hackers, leaders, and innovators of the software world
  

Featuring:

• Kirsten Lum – Twitter, LinkedIn
• Chris Benson – Twitter, GitHub, LinkedIn, Website
• Daniel Whitenack – Twitter, GitHub, Website

Show Notes:

• storytellers.ai


• Trello

Something missing or broken? PRs welcome!

Timestamps:

(00:00 ) - Opener
(00:37 ) - Welcome to Practical AI
(01:12 ) - Kirsten Lum
(05:44 ) - Selling short in data science
(08:02 ) - FUD from a management POV
(13:44 ) - Data science is like cooking
(17:32 ) - Sponsor: The Changelog
(19:16 ) - What to focus on when you're new
(22:33 ) - Managing flexibility in a small company
(26:26 ) - Navigating people in a small business
(29:17 ) - Putting the practical in PracticalAI
(35:54 ) - How to approach non data-centric people
(39:26 ) - Advantages of small ML orgs over big orgs
(42:37 ) - Mentoring people the right way
(46:00 ) - Looking into the future
(49:04 ) - Outro