On Monday, Cloudera announced the general availability of its Cloudera Data Science Workbench, a new self-service data science tool that could help boost efforts around data science and machine learning in large businesses.
The Cloudera Data Science Workbench is a secure and compliant platform, providing support for authorization, encryption, and data governance, according to a press release. It also makes essential languages like Python, R, and Scala available to data scientists through the browser. Users can download libraries and frameworks as needed to work with the latest tools.
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Companies are beginning to enter the "golden age of machine learning," with the ubiquity of data leading the charge, said Charles Zedlewski, senior vice president of products at Cloudera, in the press release. However, he added, it can be difficult for data scientists to launch and scale new projects quickly.
"The Data Science Workbench is a self-service tool that accelerates the ability to build, scale and deploy machine learning solutions using the most powerful technologies," Zedlewski said in the release. "This means that data scientists now have the freedom to share, collaborate and manage their data in a way that best suits them and their enterprise, resulting in an easier and faster path to production."
The general launch of the service comes a few days after Cloudera announced its initial public offering (IPO) on the New York Stock Exchange, seeing shares jump 20% in the first day of trading. Now, as a public company, it is essential that Cloudera maintains its momentum with the launch of new products like the Data Science Workbench.
The Cloudera Data Science Workbench was initially released in beta at the 2017 Strata + Hadoop World in San Jose, CA. It has been adopted by organizations such as the Office of National Statistics (ONS) in the UK, which used it to create statistical research that was repeatable and transferable.
"We have seen a decreased time in developing models and better visibility in tracking progress and results," Simon Sandford-Taylor, the CTO of ONS, said in the release. "We think that Cloudera Data Science Workbench has the potential to accelerate our release calendar and better share best practices."
Cloudera's Data Science Workbench also integrates with the Apache Spark deep learning library BigDL. This will help data scientists better use big data tactics on CPUs, without additional hardware investments.
The 3 big takeaways for TechRepublic readers
- Cloudera announced the general availability of its Cloudera Data Science Workbench, a new tool for data scientists that aims to make it faster to run new big data and machine learning projects.
- The workbench makes Python, R, and Scala available through the browser, and offers integration with the Apache Spark deep learning library BigDL.
- Cloudera recently went public, so the continued release of new platforms for data science and machine learning projects will be crucial to the company maintaining momentum.
- Will cloud vendors dominate machine learning? Early signs point to yes (TechRepublic)
- Strata: Cloudera, MapR and others focus on consolidating the sprawl (ZDNet)
- Understanding the differences between AI, machine learning, and deep learning (TechRepublic)
- Cloudera approaches IPO with long-term vision, but can it justify its valuation? (ZDNet)
- How Cloudera defined big data, and was defined by it (TechRepublic)
Conner Forrest has nothing to disclose. He doesn't hold investments in the technology companies he covers.
Conner Forrest is a Senior Editor for TechRepublic. He covers enterprise technology and is interested in the convergence of tech and culture.