Episode Details

The AI Dream Team: Strategies for ML Recruitment and Growth // MLOps Podcast #267 with Jelmer Borst, Analytics & Machine Learning Domain Lead, and Daniela Solis, Machine Learning Product Owner, of Picnic.

// Abstract

Like many companies, Picnic started out with a small, central data science team. As this grows larger, focusing on more complex models, it questions the skillsets & organisational setup. Use an ML platform, or build ourselves? A central team vs. embedded? Hire data scientists vs. ML engineers vs. MLOps engineers. How to foster a team culture of end-to-end ownership to balance short-term & long-term impact

// Bio

Jelmer Borst

Jelmer leads the analytics & machine learning teams at Picnic, an app-only online groceries company based in the Netherlands. Whilst his background is in aerospace engineering, he was looking for something faster-paced and found that at Picnic. He loves the intersection of solving business challenges using technology & data. In his free time loves to cook food and tinker with the latest AI developments.

Daniela Solis Morales

As a Machine Learning Lead at Picnic, I am responsible for ensuring the success of end-to-end Machine Learning systems. My work involves bringing models into production across various domains, including Personalization, Fraud Detection, and Natural Language Processing.

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Jelmer on LinkedIn: https://www.linkedin.com/in/japborst

Connect with Daniela on LinkedIn: https://www.linkedin.com/in/daniela-solis-morales/

Timestamps:

[00:00] Jelmer and Daniela's preferred coffee

[00:37] Takeaways

[03:46] Please like, share, leave a review, and subscribe to our MLOps channels!

[03:58] Use case evolution review

[08:24] Centralized ML strategy

[11:53] Managing zombie models effectively

[15:52] Clean data and collaboration

[21:07] Snowflake ML Integration options

[22:49] MLOps infrastructure components

[25:36] Pull vs. Push Adoption

[27:03] ML Model Monitoring Roles

[31:56] Inventory prediction

[36:00] Scaling machine learning teams

[42:09] Team expansion and structure

[48:20] Exploring effective team organization

[51:43] Blog reading insights

[54:25] Playing hard mode

[57:33] Wrap up