Hewlett Packard Enterprise Acquires Determined AI to Accelerate Artificial Intelligence Innovation With Fast and Simple Machine Learning Modeling
HPE will combine Determined AI’s innovative open-source AI training platform with its high performance computing solutions to enable machine learning engineers speed time-to-production for AI
Hewlett Packard Enterprise 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.
“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.”
Recommended AI News: HPE Opens IoT Innovation Lab in Geneva to Help Customers Capitalize on Edge Data
Determined AI accelerates innovation with open source AI solutions to build and train models faster and easier
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.”
Recommended AI News: Equalum Channel Growth Accelerates as Continuous Data Integration Anchors Modern IT
HPC – the foundation for delivering speed-to-insight and AI at scale
AI training is continuing to fuel projects and innovation with intelligence, and to do so effectively, and at scale, will require specialized computing. According to IDC, the accelerated AI server market, which plays an integral role in providing targeted capabilities for image and data-intensive training, is expected to grow by 28% each year and reach $18B by 2024.
The massive computing power of HPC is also increasingly being used to train and optimize AI models, in addition to combining with AI to augment workloads such as modeling and simulation, which are well-established tools to speed time-to-discovery. Intersect360 Research notes that the HPC market will grow by more than 40%, reaching almost $55 billion in revenue by 2024.
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.
The Determined AI team will join HPE’s High Performance Computing (HPC) & Mission CriticalSolutions (MCS) business group
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, and as a result of its focus on model training, Determined AI has quickly emerged as a leading player in the evolving machine learning software ecosystem. Its solution has been adopted by customers across a wide range of industries, such as biopharmaceuticals, autonomous vehicles, defense contracting, and manufacturing.
Recommended AI News: Open RF Association Drives Industry Collaboration to Develop a 5G Device Power Profile Study
[To share your insights with us, please write to sghosh@martechseries.com]
Comments are closed.