Building Computational Graphs Across Multiple Open Source Frameworks
Modern machine learning (ML) teams have found great success with combining multiple ML models as ensembles for better predictive performance. However, algorithms implemented in different ML frameworks can’t be serialized into a single combined model for deployment. In this talk, we share how DoorDash uses a computational graph approach via a domain specific language (DSL) to allow teams to use multiple frameworks at once in a single combined model. We demonstrate how we generate and interpret instances of this DSL to allow our teams to quickly define and deploy customized models and get high runtime performance during inference.
3 key takeaways:
Interoperability: Support for multiple frameworks lets teams develop models in areas of their expertise, rather than having to conform to a standard.
Accuracy: Unlocking a broad range of ensemble methods should enable greater predictive performance.
Performance: The DSL approach makes it possible to unify model implementations in C++ which will give better performance.
Arbaz is a Machine Learning Platform Engineer at DoorDash where he focuses on challenges around usability and scalability of online model serving. He has been directly involved in growing the scale of online model serving at DoorDash by more than 100x and helping multiple teams to productionize their ML business use cases. Previously, he had helped build machine learning platforms from the ground up for successful startups. Arbaz graduated from Indian Institute of Technology Kanpur (IIT-K) where he was awarded General Proficiency Medal for overall best academic performance in discipline of BTech-MTech dual degree in Computer Science.