Cloudera and Intel test solution to speed up machine learning workloads

machine learning
Credit: enzozo /

Cloudera has announced a jointly tested solution with Intel to advance capabilities for machine learning and artificial intelligence workloads.

Benchmark tests on Cloudera with Apache Spark and the newly released Intel Math Kernel Library (MKL) demonstrate the combined offering can advance machine learning performance over large data sets in less time and with less hardware, compared to OpenBLAS and F2J BLAS. This helps organizations accelerate their investments in next generation predictive analytics, Cloudera said.

By combining Apache Spark, Intel MKL libraries and Intel’s optimized CPU architecture, workloads can scale quickly. As machine learning solutions get access to more data they can provide better accuracy in delivering predictive maintenance, recommendation engines, proactive healthcare and monitoring, and risk and fraud detection.

Cloudera said machine learning helps its customers meet the demands of predictions on much larger data sets. Transamerica, for instance, uses Cloudera to test and validate data models at a much faster scale.

“There’s a growing urgency to implement more rich machine learning models to explore and solve the most pressing business problems and to impact society in a more meaningful way,” said Amr Awadallah, chief technical officer of Cloudera. “Already among our user base, machine learning is an increasingly common practice. In fact, in a recent adoption survey over 30% of respondents indicated they are leveraging Spark for machine learning.”

“As long-term business collaborators invested in each other’s technology, expanding our efforts in AI was a logical step,” said Michael Greene, vice president and general manager of the System Technologies and Optimization in Software Services Group at Intel. “Collectively, we see the future potential in AI as untapped, despite massive leaps in technology and growing implementation in recent years.”

Details on the benchmark test are available in this Cloudera blog post.

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