From Jupyter Notebook to Production: A Data Scientist's MLOps Journey | Anastasiia Kulakova
- S6E26
- 11:11
- March 15th 2026
Your model only matters if it connects to the business. But when you're a data scientist learning MLOps on the fly, experimenting on live infrastructure is terrifying.
Anastasiia Kulakova is an Amsterdam-based data scientist at JetLakes, a fast-growing energy and mobility startup. She shares her candid journey from Jupyter Notebook to production-ready ML.
In this episode:
• Why "we have the data" from stakeholders rarely means what you think
• How to build your own MLOps learning sandbox without breaking production (GitHub Actions, Heroku, DigitalOcean)
• The reality of being a data generalist at a startup — wearing every hat from model training to Scrum Master
• How JetLakes uses predictive algorithms to balance the energy grid through EV charging optimisation
• Virtual power plants: turning parked electric vehicles into grid-scale flexibility
• Why the precision economy is coming to energy — and what that means for data teams
MLTeam Success
Welcome to ML Team Success — the show for ML engineers, data scientists, and MLOps practitioners who want to actually ship AI that works in production.
I'm Ross Webb. I've led data product teams and ML engineering teams at places like Amazon and Just Eat, building platforms used by thousands of professionals. I've seen what works, what breaks, and why 90% of ML projects never make it to production.
Each episode: real conversations with practitioners who are solving the hard problems — MLOps, model deployment, inference at scale, data pipelines, and the shift to AI engineering and agentic systems.
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