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Machine learning at small organizations (Practical AI #207)
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
Notes and Links
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