New big data tools for machine learning spring from home of Spark and Mesos

New big data tools for machine learning spring from home of Spark and Mesos

New big data tools for machine learning spring from home of Spark and Mesos

If the University of California, Berkeley's AMPLab doesn't ring bells, perhaps some of its projects will: Spark and Mesos.

AMPLab was planned all along as a five-year computer science research initiative, and it closed down as of last November after running its course. But a new lab is opening in its wake: RISELab, another five-year project at UC Berkeley with major financial backing and the stated goal of "focus[ing] intensely for five years on systems that provide Real-time Intelligence with Secure Execution [RISE]."

AMPLab was created with "a vision of understanding how machines and people could come together to process or to address problems in data -- to use data to train rich models, to clean data, and to scale these things," said Joseph E. Gonzalez, Assistant Professor in the Department of Electrical Engineering and Computer Science at UC Berkeley.

RISELab's web page describes the group's mission as "a proactive step to move beyond big data analytics into a more immersive world," where "sensors are everywhere, AI is real, and the world is programmable." One example cited: Managing the data infrastructure around "small, autonomous aerial vehicles," whether unmanned drones or flying cars, where the data has to be processed securely at high speed.

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Other big challenges Gonzalez singled out include security, but not the conventional focus on access controls. Rather, it involves concepts like "homomorphic" encryption, where encrypted data can be worked without first having to decrypt it. "How can we make predictions on data in the cloud," said Gonzalez, "without the cloud understanding what it is it's making predictions about?"

Though the lab is in its early days, a few projects have already started to emerge:

Machine learning involves two basic kinds of work: Creating models from which predictions can be derived and serving up those predictions from the models. Clipper focuses on the second task and is described as a "general-purpose low-latency prediction serving system" that takes predictions from machine learning frameworks and serves them up with minimal latency.

Clipper has three aims that ought to draw the attention of anyone working with machine learning: One, it accelerates serving up predictions from a trained model. Two, it provides an abstraction layer across multiple machine learning frameworks, so a developer only has to program to a single API.

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