The News: HOUSTON – June 21, 2021 – Hewlett Packard Enterprise (NYSE: HPE) today announced that it has acquired Determined AI, a San Francisco-based startup that delivers a powerful and robust software stack to train AI models faster, at any scale, using its open source machine learning (ML) platform.
HPE will combine Determined AI’s unique software solution with its world-leading AI and high performance computing (HPC) offerings to enable ML engineers to easily implement and train machine learning models to provide faster and more accurate insights from their data in almost every industry. Read the full release from the HPE Newsroom.
Analyst Take: HPE opened up its big Discover 2021 week with a handful of announcements. One around new integrations with Azure, and the other an acquisition of Determined AI. The deal size wasn’t disclosed, but I immediately see this deal as a strategic capability to add to the company’s HPC and AI portfolio and something that will be well suited to be incorporated into the company’s as-a-service ambitions. Furthermore, the Determined AI acquisition comes at an opportune moment as HPE continues to accelerate the process of transforming its entire portfolio to consumption services as part of its GreenLake portfolio.
Determined AI is only about 5 years old, and it intends to expedite the process of model training. The company has built its offering to be friendly for both on-prem and cloud workloads. Determined AI has a vast set of capabilities to bridge data prep using Hadoop, Spark, AWS S3, and more, streamlining the model development and training cycle to deploy models using various web services and applications.
Overall, I believe that HPE’s acquisition of Determined AI will be a value add to the portfolio. Of course, I will need to get closer to the offering and then fully digest the inclusion in HPE’s go-to-market, but I envision this to be a great augmentation to the GreenLake solution mix. Technology-wise, Determined AI’s open source machine learning training platform appears to have key capabilities that will help researchers and scientists focus on innovation and shorten the typically long-tail timelines for model development and training by removing the complexity and cost associated with machine learning development.
Futurum Research provides industry research and analysis. These columns are for educational purposes only and should not be considered in any way investment advice. Neither the Author or Futurum Research holds any positions in any companies mentioned in this article.