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Gottam Sai Bhrath, a Senior Machine Learning Engineer, and Cole Bailey, an ML Platform Engineering Manager at Delivery Hero, dive into the intricacies of optimizing machine learning practices across their global operations. They discuss the evolution of feature stores, balancing centralized and decentralized models, and overcoming technological integration challenges in a diverse organization. The conversation highlights their collaborative approach to building a Global Feature Store, addressing real-time data processing and strategies to maintain data integrity in a complex environment.
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Quick takeaways
- Delivery Hero has shifted to a decentralized MLOps approach, empowering local teams to innovate solutions tailored to their specific contexts.
- The initiative to create a global feature store emphasizes collaboration across departments to improve data sharing and operational efficiency.
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Transition from Centralized to Decentralized Teams
Delivery Hero has transitioned from a centralized machine learning platform team to a more decentralized approach where individual local teams are encouraged to develop solutions tailored to their specific needs. Initially, the centralized model faced challenges due to varied demands from multiple teams, leading to difficulties in prioritizing projects effectively. As a response, the organization began empowering local teams to address their unique problems while collaborating on solutions that could have global applications. This approach not only enhances focus on localized issues but also fosters innovation and builds a more cooperative environment across diverse departments.
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