Production-targeted Spark guidance with real-world use cases Spark: Big Data Cluster
Computing in Production goes beyond general Spark overviews to provide targeted …

Choosing the optimizer is considered to be among the most crucial design decisions in deep
learning, and it is not an easy one. The growing literature now lists hundreds of optimization …

Big data processing systems such as Spark are employed in an increasing number of
diverse applications-such as machine learning, graph computation, and scientific computing …

Vizier is the de-facto blackbox optimization service across Google, having optimized some of
Google's largest products and research efforts. To operate at the scale of tuning thousands …

The ever-growing demand and complexity of machine learning are putting pressure on
hyper-parameter tuning systems: while the evaluation cost of models continues to increase …

With the dramatic increase in internet users and their demand for real-time network
performance, Spark has distributed computing environment has emerged. It is widely used …

As increasingly large volumes of raw data are generated at geographically distributed
datacenters, they need to be efficiently processed by data analytic jobs spanning multiple …

In this paper, we propose and evaluate a simple mechanism to accelerate iterative machine
learning algorithms implemented in Hadoop map-reduce (stock), and Apache Spark. In …

Spark is at the heart of today's Big Data revolution, helping data professionals supercharge
efficiency and performance in a wide range of data processing and analytics tasks. In this …

Apache Spark, popularly known for big data processing capability, is a distributed open-
source platform that uses the concept of distributed memory to facilitate big data processing …