Data center News

HPE Buys AI Startup Pachyderm To Build New AI, ML Platform

Mark Haranas

HPE will create a new machine learning pipeline and platform to accelerate customers’ AI adoption with the purchase of startup Pachyderm.


Hewlett Packard Enterprise is acquiring artificial intelligence startup Pachyderm with plans to make a new integrated AI platform to enhance its “AI-at-scale” portfolio.

The Spring, Texas-based infrastructure giant will combine Pachyderm’s reproducible AI capabilities into one integrated platform with HPE’s supercomputing technologies and the HPE Machine Learning Development Environment.

The goal is to create an integrated machine learning pipeline and platform to accelerate customers’ AI adoption—from proof of concept to production—with higher efficiency and lower costs.

[Related: 10 New Google Cloud Programs, Certs, Incentives For Partners]

“Pachyderm’s unique reproducible AI software augments HPE’s existing AI-at-scale offerings to automate and accelerate AI and unlock greater opportunities in image, video and text analysis, generative AI and other emerging large-language-model needs to realize transformative outcomes,” said Justin Hotard, executive vice president and general manager of HPC and AI at HPE, in a statement.

HPE Machine Learning Development Environment is a machine learning software solution that allows users to rapidly develop, iterate and scale high-quality models from proof of concept to production.

In addition, HPE will integrate Pachyderm with upcoming versions of its HPE Machine Learning Development System, which aims to eliminate the complexity and cost in building and training models.

Financial details of the Pachyderm deal were not disclosed.

The acquisition is expected to be completed this month.

The New HPE-Pachyderm

Pachyderm is a San Francisco-based AI startup that offers open-source reproducing AI software that provides tools for creating and managing enterprise-scale ML and AI projects.

Reproducing an AI or ML pipeline enables the use of the same data set to achieve the same results each time to increase transparency, trustworthiness and accuracy in predictions.

HPE’s Hotard said as AI projects become larger and involve complex data sets, data scientists will need reproducible AI solutions to maximize their “machine learning initiatives, optimize their infrastructure cost and ensure data is reliable and safe no matter where they are in their AI journey.”

By integrating Pachyderm’s capabilities with HPE’s existing AI offerings, the company will enable faster development and deployment of more accurate and performant large-scale AI applications.

Customers will see benefits around data lineage, data versioning and more efficient incremental data processing, HPE said.

In February, HPE invested in Pachyderm through its venture capital arm, Hewlett Packard Pathfinder, to speed time-to-market AI innovation at lower processing and operating costs.

No Nutanix Acquisition Ahead

HPE’s acquisition of Pachyderm comes after HPE recently shut down speculation that it was looking at acquiring hyperconverged infrastructure and hybrid cloud software standout Nutanix.

In December, HPE confirmed to CRN that “there are no discussions with Nutanix” regarding a potential purchase.

Nutanix CEO Rajiv Ramaswami told CRN in early December that Nutanix was “flattered” by the market’s rumors of a potential takeover but said he would not comment on speculation.

“Now, in terms of the rumors and speculation, look, it’s not appropriate for me to comment, but we are flattered by all the attention we’re getting,” Ramaswami told CRN. “In some ways, it’s a testament to the fact that we’re doing something good in the marketplace. And we’re doing something that our customers like.”

Mark Haranas

Mark Haranas is an assistant news editor and longtime journalist now covering cloud, multicloud, software, SaaS and channel partners at CRN. He speaks with world-renown CEOs and IT experts as well as covering breaking news and live events while also managing several CRN reporters. He can be reached at

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