Hewlett Packard Enterprise (HPE) has acquired open source machine learning (ML) pipeline automation startup Pachyderm. Financial terms of the deal were not disclosed, but HPE said the transaction is not subject to any regulatory approvals and is expected to close this month.
This is technology M&A deal number 17 that ChannelE2E has covered so far in 2023. See more than 2,000 technology M&A deals involving MSPs, MSSPs & IT service providers listed here.
HPE Expands AI Portfolio With Pachyderm Acquisition
Pachyderm, founded in 2014, is based in San Francisco, California. The company has 55 employees listed on LinkedIn. Pachyderm’s areas of expertise include machine learning, artificial intelligence, MLOps, data engineering, data science, AI, data versioning, data lineage, data pipelines and DataOps.
HPE will integrate Pachyderm’s software with its existing supercomputing and AI solutions to automate reproducible machine learning pipelines for large-scale AI applications, the company said.
Reproducing a machine learning pipeline enables the use of the same dataset to achieve the same results each time to increase transparency, trustworthiness and accuracy in predictions. It is critical to successful AI-at-scale initiatives, and HPE is betting that demand these technologies will increase along with the need to build and train larger machine learning models that require a high volume of complex data.
Justin Hotard, executive vice president and general manager, HPC and AI, at HPE, commented on the news:
“As AI projects become larger and increasingly involve complex data sets, data scientists will need reproducible AI solutions to efficiently 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. 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.”