Why is your model stuck in the lab? How to move your model from the lab to production
summary
Models stall because alignment and change work are missing not because algorithms fail; eight of sixteen top obstacles are nontechnical. Process: start with clear problem statements, do EDA in Jupyter and Python or Power BI to reveal data gaps, build a risk framework for injury prediction tied to a corporate safety goal, deploy via cloud storage and Salesforce integration or APIs, expose scores inside workflows, use agile prototypes to gain management trust — the model then spreads to crew assignment and reduces incidents.
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chapters (12)
- 0:00Introduction and speaker intro
- 1:12Why models get stuck in the lab
- 5:26Top challenges in ML model deployment
- 11:03Solving model deployment challenges
- 18:27Real-world use case walkthrough
- 27:43Key takeaways for moving models to production
- 30:15Q&A session begins
- 31:01Understanding client expectations
- 33:08Focusing on the biggest business problems
- 34:32Transitioning from analyst to scientist
- 37:01Technology stack for production
- 39:09Closing remarks
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