Hewlett Packard Enterprise Co. today is expanding its reach into artificial intelligence development with a software platform that supports the full lifecycle of machine learning model construction and deployment using the self-contained software environments called containers.
HPE ML Operations provides for the rapid rollout of machine learning workloads across on-premises, public cloud and hybrid cloud environments. The idea is to enable development teams to employ processes similar to those used in DevOps, the rapid application-building technique that that involves frequent code releases and constant refinement. The result is reductions in model deployment times from months to days, HPE said.
The company is attacking a common problem with machine learning projects, which is a lack of resources and operational processes to deploy them. Gartner Inc. estimates that half of machine learning projects fail to be fully deployed due to lack of operational support. Part of the problem is that machine learning models can be complex and require large amounts of data and computer horsepower, making provisioning both expensive and time-consuming.
HPE figures containers can help. It’s building on top of the Epic software platform it acquired with the purchase of BlueData Software Inc. late last year. Epic includes pre-configured versions of popular AI and analytics applications that are packaged into software containers that can be stored in a library and quickly put into service.
ML Operations is said to cover the full lifecycle of machine learning development, including model building, training, deployment, monitoring and team collaboration. Developers have access to self-service “sandbox” environments for tools and data science notebooks. Completed models can be reproduced and reused. Development teams get full visibility across the model lifecycle and security includes multi-tenancy and integration with enterprise authentication mechanisms.
HPE said ML Operations 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 its ecosystem partners including Dataiku Inc. and H2O.ai Inc.
Pricing wasn’t specified.
Image: A Health Blog/Flickr
Since you’re here …
… We’d like to tell you about our mission and how you can help us fulfill it. SiliconANGLE Media Inc.’s business model is based on the intrinsic value of the content, not advertising. Unlike many online publications, we don’t have a paywall or run banner advertising, because we want to keep our journalism open, without influence or the need to chase traffic.The journalism, reporting and commentary on SiliconANGLE — along with live, unscripted video from our Silicon Valley studio and globe-trotting video teams at theCUBE — take a lot of hard work, time and money. Keeping the quality high requires the support of sponsors who are aligned with our vision of ad-free journalism content.
If you like the reporting, video interviews and other ad-free content here, please take a moment to check out a sample of the video content supported by our sponsors, tweet your support, and keep coming back to SiliconANGLE.