DataFlow: A Framework for High-Performance Streaming ML
Published:Dec 30, 2025 04:24
•1 min read
•ArXiv
Analysis
This paper introduces DataFlow, a framework designed to bridge the gap between batch and streaming machine learning, addressing issues like causality violations and reproducibility problems. It emphasizes a unified execution model based on DAGs with point-in-time idempotency, ensuring consistent behavior across different environments. The framework's ability to handle time-series data, support online learning, and integrate with the Python data science stack makes it a valuable contribution to the field.
Key Takeaways
- •DataFlow aims to unify batch and streaming ML workflows.
- •It uses DAGs with point-in-time idempotency to ensure consistent behavior.
- •The framework supports online learning, caching, and parallelization.
- •It integrates with the Python data science stack.
Reference
“Outputs at any time t depend only on a fixed-length context window preceding t.”