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Cloudera Accelerates Enterprise Machine Learning from Research to Production

 

Cloudera, Inc. (NYSE: CLDR), the modern platform for machine learning and analytics optimized for the cloud, today announced new innovations to help businesses operationalize data insights faster by making data scientists and data engineers more productive. New machine learning capabilities make it easier for data scientists to quickly train and deploy models with higher confidence and lower risk. Massive increases in performance, scale and capacity of Cloudera’s modern data platform help organizations keep pace with the explosive growth and diversity of data in their business. These new capabilities enable data teams to collaborate more effectively and deliver models to production faster, enabling secure access to enterprise-scale, high-performance data and compute, both on-premises and across public clouds.

“We believe data can make what is impossible today, possible tomorrow. With enhanced capabilities in machine learning, analytics, and cloud, the new software products and cloud services we are announcing will enable our customers to more rapidly gain competitive advantages in the data economy,” said Tom Reilly, Chief Executive Officer at Cloudera. “These enhancements demonstrate Cloudera’s commitment to market-leading innovations that empower enterprises to securely transform complex data into clear and actionable insights to propel their digital transformation.”

Enhancements announced today include:

Cloudera Data Science Workbench 1.4 Accelerates Everyday Workflows for Data Scientists

Cloudera Data Science Workbench 1.4 extends the self-service machine learning platform from research to production. Typically, data science teams struggle to operationalize their work. It’s difficult to reliably reproduce or deploy trained models without inefficient and error-prone reimplementation. With this latest release, data scientists can run and track versioned experiments, as well as deploy models as REST APIs with a single click. Data teams now have a unified workflow to build, train, compare, and deploy models to production on a common, secure platform that runs anywhere.

“We’re thrilled to be launching new capabilities in Cloudera Data Science Workbench that accelerate everyday workflows for data scientists, including experiment management and model deployment, with a seamless experience that also keeps data secure and under governance,” said Hilary Mason, GM, Machine Learning at Cloudera.

Cloudera Data Science Workbench 1.4 will be available this summer. Schedule a demo today.

Cloudera Altus Powers Machine Learning on Microsoft Azure

Today, we are excited to announce Cloudera Altus is now generally available on Microsoft Azure, making Altus the industry’s first multi-cloud, multi-function PaaS. Cloudera Altus offers Data Engineering for Azure, which simplifies and speeds ETL (extract, transform, load), data processing, and batch machine learning by reducing complexity, enabling engineers and developers to deliver more diverse data to data scientists, analytics teams, and downstream data products. Azure customers can also take advantage of the shared data catalog capabilities in Cloudera Altus SDX (beta), that preserve business metadata and security and governance policies so they can be applied consistently across data processing and analytics workloads in the cloud.

“Cloudera continues to be a strategic partner for us as we build out our cloud-based platform, which will support one of Europe’s largest and most powerful IoT data marketplaces,” said Dr. Ingo Hofacker, who is responsible for IoT business at Deutsche Telekom, a customer and beta user of Cloudera Altus Data Engineering on Azure. “We can establish and run data pipelines that support mission-critical machine learning and analytics applications with ease and at a faster pace with Cloudera Altus Data Engineering on Azure.”

Cloudera Altus Analytic DB, the first data warehouse cloud service that brings the warehouse to the data, is now also available on Microsoft Azure (beta). It delivers instant self-service BI and SQL analytics to anyone easily, reliably, and securely. Furthermore, by leveraging Altus SDX, the same data and catalog are accessible for analysts, data scientists, data engineers, and others using the tools they prefer—SQL, Python, R—all without any data movement.

Start a 30-day free trial of Cloudera Altus for Microsoft Azure now.

Cloudera’s Most Powerful Platform for Machine Learning and Analytics

Cloudera Enterprise 6.0 delivers significant advances in performance and enterprise quality, showcasing innovations in search, streaming, scale, and control—all designed to help businesses operationalize data insights faster. Cloudera continues to invest the time and expertise to test, curate and contribute to the projects our customers deem valuable. The latest release is Cloudera’s most powerful platform for mission-critical machine learning and analytics applications, capable of running on-premises, in the cloud, or wherever the data resides. Release 6.0 also introduces GPU support in the cluster and Hive optimizations to significantly accelerate machine learning and data engineering applications as compared to the previous release—improving end user productivity and further reducing infrastructure costs.

“Business leaders realize the intrinsic value of data, but increasingly, it is becoming difficult if not impossible for them to take advantage of the latest innovations such as machine learning and real-time streaming with traditional, single-purpose analytic platforms. Meanwhile, cloud service providers have stirred up the pot by opening the possibility to mixing and matching resources to fit the problem, however, cloud providers lack the ability to provide a single shared governance environment spanning from on-premises to the cloud,” said Tony Baer, principal analyst, IT, at Ovum.

“Data is the lifeblood of our business and our industry. Cloudera enables our data processing and analytics, creating the scale to match our data growth and the speed to enable real-time insights in an enterprise-ready, secure platform,” says Steve Hirsch, Chief Data Officer, Intercontinental Exchange, parent company of the New York Stock Exchange.

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