HPE Discover was held this week, virtually, of course. I can’t wait for the return of in-person conferences. It’s easier for me to get relevant conversations and learn from technology users when you’re all gathered together. You attend on demand here.

I didn’t have any specific industrial/manufacturing discussions this year, although I had met up with Dr. Tom Bradicich earlier to get the latest on IoT and Edge. You can check out that conversation here.

I suppose the biggest company news was the acquisition of Determined AI (see news release below). This year’s theme Age of Insight (into data) and AI and ML are technologies required to pull insight out of the swamp of data.

HPE’s strategy remains to be an as-a-Service company. This strategy is gaining momentum. They announced 97% customer retention with Greenlake, the cloud-as-a-service platform. We are seeing an uptake of this strategy in specifically manufacturing software companies, so I hope you manufacturing IT people are studying this.

Dr. Eng Lim Goh, CTO, stated in his keynote, “We are awash in data, but it is siloed. This brings a need for a federation layer.” Later, in the HPE Labs keynote, the concept of Dataspace was discussed. My introduction to that concept came from a consortium in Europe. More on that in a bit. Goh gazed into the future predicting that we need to know what data to collect, and then look at how and where to collect and find and store data.

The HPE Labs look into Dataspaces highlighted these important characteristic: democratize data access; lead with open source; connect data producers/consumers; and remove silos. Compute can’t keep up with amount of data being generated, therefore the need for the exascale compute HPE is developing. Further, AI & ML are critical capability, but data is growing too fast to train it.

The Labs presentation brought out the need to think differently about programming in the future. There was also a look into future connectivity—looking at photonics research. This technology will enhance data movement, increase bandwidth with low power consumption. To realize the benefits, engineers will have to realize it’s more than wire-to-wire exchange. This connectivity opens up new avenues of design freedom. Also to obtain best results for exploiting this technology for data movement companies and universities must emphasize cross-disciplinary training.

Following is the news release on the Determined AI acquisition.

HPE acquires Determined AI to accelerate artificial intelligence innovation

Hewlett Packard Enterprise has acquired Determined AI, a San Francisco-based startup that delivers a 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.  

“As we enter the Age of Insight, our customers recognize the need to add machine learning to deliver better and faster answers from their data,” said Justin Hotard, senior vice president and general manager, HPC and Mission Critical Solutions (MCS), HPE. “AI-powered technologies will play an increasingly critical role in turning data into readily available, actionable information to fuel this new era. Determined AI’s unique open source platform allows ML engineers to build models faster and deliver business value sooner without having to worry about the underlying infrastructure. I am pleased to welcome the world-class Determined AI team, who share our vision to make AI more accessible for our customers and users, into the HPE family.”

Building and training optimized machine learning models at scale is considered the most demanding and critical stage of ML development, and doing it well increasingly requires researchers and scientists to face many challenges frequently found in HPC. These include properly setting up and managing a highly parallel software ecosystem and infrastructure spanning specialized compute, storage, fabric and accelerators. Additionally, users need to program, schedule and train their models efficiently to maximize the utilization of the highly specialized infrastructure they have set up, creating complexity and slowing down productivity.

Determined AI’s open source machine learning training platform closes this gap to help researchers and scientists to focus on innovation and accelerate their time to delivery by removing the complexity and cost associated with machine learning development. This includes making it easy to set-up, configure, manage and share workstations or AI clusters that run on-premises or in the cloud.


Determined AI also makes it easier and faster for users to train their models through a range of capabilities that significantly speed up training, which in one use case related to drug discovery, went from three days to three hours. These capabilities include accelerator scheduling, fault tolerance, high speed parallel and distributed training of models, advanced hyperparameter optimization and neural architecture search, reproducible collaboration and metrics tracking.

“The Determined AI team is excited to join HPE, who shares our vision to realize the potential of AI,” said Evan Sparks, CEO of Determined AI. “Over the last several years, building AI applications has become extremely compute, data, and communication intensive. By combining with HPE’s industry-leading HPC and AI solutions, we can accelerate our mission to build cutting edge AI applications and significantly expand our customer reach.” To tackle the growing complexity of AI with faster time-to-market, HPE is committed to continue delivering advanced and diverse HPC solutions to train machine learning models and optimize applications for any AI need, in any environment. By combining Determined AI’s open source capabilities, HPE is furthering its mission in making AI heterogeneous and empowering ML engineers to build AI models at a greater scale.

Additionally, through HPE GreenLake cloud services for High Performance Computing (HPC), HPE is making HPC and AI solutions even more accessible and affordable to the commercial market with fully managed services that can run in a customer’s data center, in a colocation or at the edge using the HPE GreenLake edge to cloud platform.

Determined AI was founded in 2017 by Neil Conway, Evan Sparks, and Ameet Talwalkar, and based in San Francisco. It launched its open-source platform in 2020.

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