HPE Accelerates Artificial Intelligence Innovation with Enterprise-Grade Solution for Managing Entire Machine Learning Lifecy...
10 Septiembre 2019 - 06:45AM
Business Wire
New HPE Machine Learning (ML) Ops solution speeds time-to-value
for AI from months to days and brings DevOps agility to the ML
model lifecycle
Hewlett Packard Enterprise (HPE) today announced a
container-based software solution, HPE ML Ops, to support the
entire machine learning model lifecycle for on-premises, public
cloud and hybrid cloud environments. The new solution introduces a
DevOps-like process to standardize machine learning workflows and
accelerate AI deployments from months to days.
The new HPE ML Ops solution extends the capabilities of the
BlueData EPIC™ container software platform, providing data science
teams with on-demand access to containerized environments for
distributed AI / ML and analytics. BlueData was acquired by HPE in
November 2018 to bolster its AI, analytics, and container
offerings, and complements HPE’s Hybrid IT solutions and HPE
Pointnext Services for enterprise AI deployments.
Enterprise AI adoption has more than doubled in the last four
years1, and organizations continue to invest significant time and
resources in building machine learning and deep learning models for
a wide range of AI use cases such as fraud detection, personalized
medicine, and predictive customer analytics. However, the biggest
challenge faced by technical professionals is operationalizing ML,
also known as the “last mile,” to successfully deploy and manage
these models, and unlock business value. According to Gartner, by
2021, at least 50 percent of machine learning projects will not be
fully deployed due to lack of operationalization.2
HPE ML Ops transforms AI initiatives from experimentation and
pilot projects to enterprise-grade operations and production by
addressing the entire machine learning lifecycle from data
preparation and model building, to training, deployment,
monitoring, and collaboration.
“Only operational machine learning models deliver business
value,” said Kumar Sreekanti, SVP and CTO, Hybrid IT at HPE. “And
with HPE ML Ops, we provide the only enterprise-class solution to
operationalize the end-to-end machine learning lifecycle for
on-premises and hybrid cloud deployments. We’re bringing DevOps
speed and agility to machine learning, delivering faster
time-to-value for AI in the enterprise.”
“From retail to banking to manufacturing to healthcare and
beyond, virtually all industries are adopting or investigating
AI/ML to develop innovative products and services and gain a
competitive edge. While most businesses are ramping up on the build
and train phase of their AI/ML projects, they are struggling to
operationalize the entire ML lifecycle from PoC to pilot to
production deployment and monitoring,” said Ritu Jyoti, program
vice president, Artificial Intelligence (AI) Strategies at IDC.
“HPE is closing this gap by addressing the entire ML lifecycle with
its container-based, platform-agnostic offering – to support a
range of ML operational requirements, accelerate the overall time
to insights, and drive superior business outcomes.”
“Our online games generate billions of data points every day,”
says Alex Ryabov, head of Data Services at Wargaming. “Using
complex ML models, our data scientists leverage this data for
prescriptive analytics to improve our players’ experience, lifetime
value, and loyalty. With HPE’s BlueData software, we’re
containerizing these ML and analytics environments to help improve
operational efficiency and optimize our business.”
With the HPE ML Ops solution, data science teams involved in
building and deploying ML models can benefit from the industry’s
most comprehensive operationalization and lifecycle management
solution for enterprise AI:
- Model Build: Pre-packaged, self-service sandbox
environments for ML tools and data science notebooks
- Model Training: Scalable training environments with
secure access to data
- Model Deployment: Flexible and rapid deployment with
reproducibility
- Model Monitoring: End-to-end visibility across the ML
model lifecycle
- Collaboration: Enable CI/CD workflows with code, model,
and project repositories
- Security and Control: Secure multi-tenancy with
integration to enterprise authentication mechanisms
- Hybrid Deployment: Support for on-premises, public
cloud, or hybrid cloud
The HPE ML Ops solution works with a wide range of open source
machine learning and deep learning frameworks including Keras,
MXNet, PyTorch, and TensorFlow as well as commercial machine
learning applications from ecosystem software partners such as
Dataiku and H2O.ai.
“As a longtime partner with HPE Pointnext Services, we are very
excited that BlueData is now part of HPE,” said Florian Douetteau,
CEO of Dataiku. “At Dataiku, we strive to bring large-scale
adoption of machine learning to all enterprises. The combination of
Dataiku with HPE’s BlueData software will help our customers to
successfully scale and operationalize their machine learning
projects, delivering real impact for their business.”
To learn more about HPE ML Ops, please visit:
hpe.com/info/MLOps
Availability
HPE ML Ops is generally available now as a software
subscription, together with HPE Pointnext Services and customer
support.
Additional Resources
- Learn more in the blog post: Machine Learning
Operationalization in the Enterprise
- Attending the O’Reilly AI Conference in San Jose? View a demo
at the HPE booth and join the session Unlock Your Data’s Value with
AI on September 11
- Join a live virtual event on September 17 at 9am PDT / 12pm
EDT: “Machine Learning Operations” #MLOps
About Hewlett Packard Enterprise
Hewlett Packard Enterprise is a global technology leader focused
on developing intelligent solutions that allow customers to
capture, analyze and act upon data seamlessly from edge to cloud.
HPE enables customers to accelerate business outcomes by driving
new business models, creating new customer and employee
experiences, and increasing operational efficiency today and into
the future.
1 Source: Gartner, 2019 CIO Survey: CIOs Have Awoken to the
Importance of AI, January 3 2019.
2 Source: Gartner, A Guidance Framework for Operationalizing
Machine Learning for AI, October 24 2018.
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version on businesswire.com: https://www.businesswire.com/news/home/20190910005398/en/
Nahren Khizeran, HPE Global Product Communications
Nahren.Khizeran@hpe.com
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