The AI Dream Team: Strategies for ML Recruitment and Growth // Jelmer Borst and Daniela Solis // #267

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.// AbstractLike 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// BioJelmer BorstJelmer 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 MoralesAs 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.// MLOps Jobs board jobs.mlops.community// MLOps Swag/Merchhttps://mlops-community.myshopify.com/ --------------- ✌️Connect With Us ✌️ -------------Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch all episodes, blogs, newsletters, and more: https://mlops.community/Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Jelmer on LinkedIn: https://www.linkedin.com/in/japborstConnect 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