How to Deploy ML Models from Notebooks to Production: A Churn Prediction Case Study (feat. Nick Zervoudis)

Data Breakthroughs - Episode 3: From Notebooks to Action

Real-world data problem solving in action! Nick Zervoudis (Data Product Management Consultant & Coach) and host Lior Barak tackle a community-submitted ML deployment challenge for the first time during the recording.

Problem Category: Machine Learning & AI Implementation Runtime: 45 minutes

The Challenge: A high-accuracy churn prediction model (87%!) sits trapped in Jupyter notebooks while the sales team desperately needs daily at-risk customer alerts in Salesforce to prevent customer loss.

The Solution: A phased 90-day deployment approach with human validation, cost/revenue tracking, and strategic go/no-go decision points - prioritizing quick wins while building toward full integration.

Key Takeaways:

Always run a standard checklist for IT, legal, privacy, and data access requirements

Define success metrics upfront - what does "breakthrough" really mean for your business?

Don't wait for perfection - export data ASAP and let teams start learning from it

Guest: Nick Zervoudis, Data Product Management Consultant & Coach at Value from Data & Former Head of Product at CKDelta (doubled annual revenue, 5x ARR), Co-host of "Data Product Management in Action" podcast

Connect with Nick:

Website: https://blog.valuefromdata.ai/

Course: https://maven.com/nick-zervoudis/dpm-value-course

LinkedIn: https://www.linkedin.com/in/nzervoudis/

Get Involved: Submit your data problem: https://forms.office.com/r/KUPaLPEZwMBecome a guest: https://forms.office.com/r/Qy2riCHvT5Join the conversation: #DataBreakthrough

Full show notes & visual diagrams: [Link to newsletter version]

Disclaimer: This podcast is intended for educational and inspirational purposes. Solutions discussed are general approaches - adapt them to your specific context and constraints.

Music: "Calisson" courtesy of Riverside