NEW YORK, Dec. 5, 2018 /PRNewswire/ -- AI SUMMIT
-- 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.
Capabilities include:
- 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."
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.
Availability
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.
Additional Resources
- Read our Vision blog post: An Introduction to Federated
Learning
- Explore our interactive federated learning prototype, Turbofan
Tycoon
- Check out these on-demand webinars:
-
- Federated Learning–ML with Privacy on the Edge
- Industrialize AI with Enterprise Scale Machine Learning
- Introducing Cloudera Machine Learning: Cloudera's New
Cloud-Native Platform for Enterprise Scale Machine Learning
Connect with Cloudera
About Cloudera:
cloudera.com/about-cloudera.html
Read our VISION blog: vision.cloudera.com/ and Engineering blog:
blog.cloudera.com/
Follow us on Twitter: twitter.com/cloudera and LinkedIn:
linkedin.com/cloudera/
Visit us on Facebook: facebook.com/cloudera
See us on YouTube: youtube.com/user/clouderahadoop
Join the Cloudera Community: community.cloudera.com/
Read about our customers' successes:
cloudera.com/more/customers.html
Cloudera and associated marks and trademarks are
registered trademarks of Cloudera, Inc. All other company and
product names may be trademarks of their respective owners.
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.
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SOURCE Cloudera, Inc.