New Cloudera offering simplifies machine learning workflows with a unified experience for data engineering and data science on Kubernetes
AI SUMMIT, NEW YORK – December 5, 2018 – Cloudera, Inc., (NYSE: CLDR), the modern platform for machine learning and analytics optimized for the cloud, announced a preview of a new, next-generation, cloud-native machine learning platform powered by Kubernetes. The upcoming Cloudera Machine Learning expands Cloudera’s offerings for enterprise self-service data science. It delivers fast provisioning and autoscaling as well as containerized, distributed processing on heterogeneous compute. Cloudera Machine Learning also ensures secure data access with a unified experience across on-premises, public cloud, and hybrid environments.
Unlike data science tools that address only parts of the machine learning workflow or are only available for the public cloud, Cloudera Machine Learning combines data engineering and data science, on any data, anywhere. In addition, it breaks down data silos to simplify and accelerate the end-to-end machine learning workflow. Enterprises can request access to a pre-release version of the Cloudera Machine Learning product here, as of today.
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Containers and the Kubernetes ecosystem are enabling the agility of cloud across diverse environments with a consistent experience, unlocking scalable service delivery for IT across hybrid and multi-cloud deployments. At the same time, enterprises are looking to operationalize and scale end-to-end machine learning workflows. Cloudera Machine Learning enables enterprises to accelerate machine learning from research to production – empowering users to easily provision environments and scale resources so they can spend less time on infrastructure and more time on innovation.
- Seamless portability across private cloud, public cloud, and hybrid cloud powered by Kubernetes
- Rapid cloud provisioning and autoscaling
- Scale-out data engineering and machine learning with seamless dependency management provided by containerized Python, R, and Spark-on-Kubernetes
- High velocity deep learning powered by distributed GPU scheduling and training
- Secure data access across HDFS, cloud object stores, and external databases
“Making teams more productive is essential to scaling machine learning capabilities in the enterprise. This requires a new kind of platform to consistently build and deploy models across highly scalable, transparent infrastructure, tapping into data anywhere,” said Hilary Mason, general manager, Machine Learning at Cloudera. “Cloudera Machine Learning brings together the critical capabilities of data engineering, collaborative exploration, model training, and model deployment in a cloud-native platform that runs where you need it – all with the built-in security, governance, and management capabilities our customers require.”
“Having built mature web security systems at Akamai based on comprehensive data analysis and processing, we recognize that speed and scale are vital for running Internet-scale anomaly detection,” said Oren Marmor, DevOps Manager, Web Security at Akamai. “The agility that Docker and Kubernetes bring to Apache Spark is an important building block for us, for both data science and data engineering. We are excited to see the introduction of the upcoming Cloudera Machine Learning platform. The platform’s ability to simplify OS and library dependency management is a promising development.”
Matt Brandwein, Cloudera’s senior director of products, shares how its new, cloud-native machine learning platform empowers teams to develop and deploy machine learning at enterprise scale.
With Cloudera Machine Learning plus research and expert guidance from Cloudera Fast Forward Labs, Cloudera offers a comprehensive approach to accelerating the industrialization of AI for customers.
To help customers leverage AI everywhere, Cloudera’s applied research team recently introduced Federated Learning for deploying machine learning models from the cloud to the network edge while ensuring data privacy and reducing network communications overhead. The report offers a detailed, technical explanation of the approach along with practical engineering recommendations that address use cases across mobile, healthcare and manufacturing, including IoT-driven predictive maintenance.
“Federated learning removes blockers to the enterprise application of machine learning in highly regulated and competitive industries. We’re thrilled to be able to help our customers get a jump start on the industrialization of AI with federated learning,” said Mike Lee Williams, research engineer at Cloudera Fast Forward Labs.
Mike Lee Williams, research engineer at Cloudera Fast Forward Labs, explains how federated learning makes it possible to build machine learning systems without direct access to training data.
Cloudera Machine Learning Preview
Enterprises can request access to preview the upcoming Cloudera Machine Learning offering here today. The product is planned to release in 2019.
Cloudera Fast Forward Labs Research Report: Federated Learning
The full report is available now to paid subscribers of Cloudera Fast Forward Labs applied machine learning advising and research. Go here to find out more.
- Explore our interactive federated learning prototype, Turbofan Tycoon
- Check out these on-demand webinars:
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This press release contains forward-looking statements including, among other things, statements regarding the expected performance and benefits of Cloudera’s offerings. The words "believe," "may," "will," "plan," "expect," and similar expressions are intended to identify forward-looking statements. These forward-looking statements are subject to risks, uncertainties, and assumptions. If the risks materialize or assumptions prove incorrect, actual results could differ materially from the results implied by these forward-looking statements. Risks include, but are not limited to, risks described in our filings with the Securities and Exchange Commission (SEC), including our Form S-1 Registration Statement, and our future reports that we may file with the SEC from time to time, which could cause actual results to vary from expectations. Cloudera assumes no obligation to, and does not currently intend to, update any such forward-looking statements after the date of this release.