Scalable and Maintainable Workflows at Lyft with Flyte
Published:Jan 30, 2020 19:30
•1 min read
•Practical AI
Analysis
This article from Practical AI discusses Lyft's use of Flyte, an open-source, cloud-native platform for machine learning and data processing. The interview with Haytham AbuelFutuh and Ketan Umare, software engineers at Lyft, covers the motivation behind Flyte's development, its core value proposition, the role of type systems in user experience, its relationship to Kubeflow, and its application within Lyft. The focus is on how Flyte enables scalable and maintainable workflows, a crucial aspect for any large-scale data and ML operation. The article likely provides insights into the challenges and solutions related to building and deploying ML models in a production environment.
Key Takeaways
- •Flyte is a cloud-native platform for ML and data processing.
- •The article highlights the importance of scalable and maintainable workflows.
- •The interview provides insights into Flyte's development and its use at Lyft.
Reference
“We discuss what prompted Ketan to undertake this project and his experience building Flyte, the core value proposition, what type systems mean for the user experience, how it relates to Kubeflow and how Flyte is used across Lyft.”