ORLANDO, Fla., Oct. 17, 2018 /PRNewswire/ -- Gartner
Symposium ITxpo -- Cloudera, Inc., (NYSE: CLDR),
the modern platform for machine learning and analytics optimized
for the cloud, today announced its strategic vision to accelerate
the industrialization of enterprise machine learning (ML) and
artificial intelligence (AI)—making the process of building,
scaling, and deploying enterprise machine learning automated,
repeatable and predictable. Through Cloudera's platform for ML,
Cloudera Data Science Workbench development environment and applied
AI advising and research offerings, Cloudera has enabled hundreds
of enterprises to gain greater insight from their data, detect
anomalous activity and predict events. Now, Cloudera intends to
expand its customers' success in these areas with a vision for
industrializing AI. Cloudera customers will be able to automate
decisions at enterprise scale. Building, deploying and scaling
ML/AI applications will become repeatable with what Cloudera calls
an 'AI Factory' for turning data into decisions, at any scale,
anywhere.
Click to Tweet: Hear
@hmason talk about @Cloudera's vision for industrializing #AI and
the radical changes in store for the future. Unveiling details:
http://bit.ly/2PDOf0I #IndustrializeAI #AIFactory
#MachineLearning
"The modern enterprise will be a network of intelligent
applications powered by machine learning and AI. Right now the
effort that goes into building ML and AI solutions is heavily
skewed towards allocating and maintaining underlying
infrastructure, instead of building a compounding set of
capabilities that can deliver increased business values," said
Hilary Mason, general manager,
Machine Learning at Cloudera. "AI deployments should be boring,
meaning the process for deploying and scaling these types of
applications should be routine. We should be paying attention to
the value generated, not to the technology. This is why Cloudera is
evolving its products and platform to industrialize the process of
delivering AI solutions at enterprise scale."
Today, no single platform unifies and powers all ML and AI
workflows. Isolated ML and AI projects that employ disparate
technology stacks result in duplicated efforts across the
enterprise. Siloed infrastructure introduces quality issues and
risk associated with security, governance, and compliance. Reliance
on bespoke solutions comes at the cost of internal skill building
and differentiation. Lock-in to single vendor environments can
limit the flexibility and agility needed to innovate and capitalize
on new business opportunities. These common challenges present
barriers to successfully operationalizing and scaling ML/AI
capabilities at enterprise
scale.
Industrialized enterprise ML and AI resolves these challenges,
empowering businesses to build a repeatable AI Factory for turning
data into decisions, at any scale, anywhere. Such an AI Factory
requires a modern technical foundation; a platform for managing
connected data workflows across multiple cloud and on-premise
environments. An AI Factory enables businesses to own and protect
their data and intellectual property, maintaining control of their
future and injecting AI into every part of the business that can be
automated—reliably, predictably and securely.
Building on deep experience in machine learning, analytics and
cloud, Cloudera plans to accelerate its roadmap to help customers
more rapidly capitalize on the value of their data with ML and AI
capabilities. Cloudera will deliver seamless, elastic, end-to-end
machine learning workflows spanning public and private clouds as
part of its recently announced enterprise data cloud strategy.
Cloudera will also expand support for the latest open data science
tools, from Python and R to Spark and Tensorflow, to streamline
unified data pipeline and AI model management at scale while
minimizing cost and risk.
Unveiling of Industrialized AI with Enterprise Scale
Machine Learning Vision at Gartner Symposium
On October 17 at 3:15 - 4:00
pm ET, Mason will present her vision of Industrialized AI
with Enterprise Scale Machine Learning at the Gartner Symposium
ITxpo. If unable to make the event in Orlando, register for Mason's live webinar on
November 7, "Industrialized AI with
Enterprise Scale Machine Learning" and available on-demand
after.
Additional Resources
- Watch an on-demand webinar Demystifying ML & AI on how to
identify and execute on practical machine learning opportunities,
presented by Hilary Mason
- Get practical guidance and clarity with Cloudera Fast Forward
Labs Research
- Learn more about Cloudera's Machine Learning Advisory
Services
Connect with Cloudera
About Cloudera:
cloudera.com/more/about.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/featured
Join the Cloudera Community: community.cloudera.com/
Read about our customers' successes:
cloudera.com/more/customers.html
For information on Cloudera Enterprise and SDX:
cloudera.com/products/sdx.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.