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Found 5 papers, 2 papers with code
InferLine: ML Inference Pipeline Composition Framework
The dominant cost in production machine learning workloads is not training individual models but serving predictions from increasingly complex prediction pipelines spanning multiple models, machine learning frameworks, and parallel hardware accelerators.
Distributed, Parallel, and Cluster Computing
Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning
We explore how Deep Neural Networks can represent meta-policies that switch among a set of previously learned policies, specifically in settings where the dynamics of a new scenario are composed of a mixture of previously learned dynamics and where the state observation is possibly corrupted by sensing noise.
IDK Cascades: Fast Deep Learning by Learning not to Overthink
Advances in deep learning have led to substantial increases in prediction accuracy but have been accompanied by increases in the cost of rendering predictions.
Clipper: A Low-Latency Online Prediction Serving System
In this paper, we introduce Clipper, a general-purpose low-latency prediction serving system.
The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox
In this work, we present Velox, a new component of the Berkeley Data Analytics Stack.
Databases
