  Machine learning inference pipelines commonly encountered in data science and
industries often require real-time responsiveness due to their user-facing
nature. However, meeting this requirement becomes particularly challenging when
certain input features require aggregating a large volume of data online.
Recent literature on interpretable machine learning reveals that most machine
learning models exhibit a notable degree of resilience to variations in input.
This suggests that machine learning models can effectively accommodate
approximate input features with minimal discernible impact on accuracy. In this
paper, we introduce Biathlon, a novel ML serving system that leverages the
inherent resilience of models and determines the optimal degree of
approximation for each aggregation feature. This approach enables maximum
speedup while ensuring a guaranteed bound on accuracy loss. We evaluate
Biathlon on real pipelines from both industry applications and data science
competitions, demonstrating its ability to meet real-time latency requirements
by achieving 5.3x to 16.6x speedup with almost no accuracy loss.
