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Found 11 papers, 1 papers with code
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
Clipper: A Low-Latency Online Prediction Serving System
In this paper, we introduce Clipper, a general-purpose low-latency prediction serving system.
KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics
Modern advanced analytics applications make use of machine learning techniques and contain multiple steps of domain-specific and general-purpose processing with high resource requirements.
Scalable Linear Causal Inference for Irregularly Sampled Time Series with Long Range Dependencies
Linear causal analysis is central to a wide range of important application spanning finance, the physical sciences, and engineering.
ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models
Data cleaning is often an important step to ensure that predictive models, such as regression and classification, are not affected by systematic errors such as inconsistent, out-of-date, or outlier data.
MLlib: Machine Learning in Apache Spark
Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks.
TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries
The proliferation of massive datasets combined with the development of sophisticated analytical techniques have enabled a wide variety of novel applications such as improved product recommendations, automatic image tagging, and improved speech-driven interfaces.
Active Learning for Crowd-Sourced Databases
Based on this observation, we present two new active learning algorithms to combine humans and algorithms together in a crowd-sourced database.
MLI: An API for Distributed Machine Learning
MLI is an Application Programming Interface designed to address the challenges of building Machine Learn- ing algorithms in a distributed setting based on data-centric computing.
DLHub: Model and Data Serving for Science
Here we present the Data and Learning Hub for science (DLHub), a multi-tenant system that provides both model repository and serving capabilities with a focus on science applications.
Understanding and Optimizing Packed Neural Network Training for Hyper-Parameter Tuning
As neural networks are increasingly employed in machine learning practice, how to efficiently share limited training resources among a diverse set of model training tasks becomes a crucial issue.
