MLOps.community The AI Dream Team: Strategies for ML Recruitment and Growth // Jelmer Borst and Daniela Solis // #267
Oct 9, 2024
Jelmer Borst, analytics and machine learning leader at Picnic, and Daniela Solis Morales, machine learning lead, delve into the dynamics of building effective ML teams. They discuss shifting from decentralized to centralized structures and the challenges of recruiting the right talent. The pair explores the complexities of demand forecasting in online grocery delivery and stresses the importance of collaboration between data scientists and business teams. They also highlight the need for lightweight, scalable ML infrastructure and the evolving roles within data science to meet business goals.
AI Snips
Chapters
Transcript
Episode notes
Clean Data at Picnic
- Picnic prioritized building a data warehouse with clean data from the start, unlike many companies that focused on data lakes.
- This allowed data scientists to spend more time building models and less time cleaning data, leading to faster iteration and more models in production.
Complexity of Model Scaling
- Scaling ML models introduces complexities beyond organizational and platform concerns.
- Companies often face challenges with "zombie models" where the impact is unclear, making it risky to decommission them due to potential revenue hits.
End-to-End Ownership for ML Engineers
- ML engineers should focus on end-to-end ownership, ensuring models deliver business value and not just predictions.
- Consider model fallbacks, safeguards, and the impact of model downtime during the design phase.