Many enterprises around the world are discovering new insights, revenue and efficiencies through the use of artificial intelligence (AI). At the same time, companies are discovering that they can accelerate their projects by adjusting their infrastructure approach. These changes have helped to create new opportunities and growth options, as well as preventing a trip to the pile of AI failures.
Here are some recent examples of companies that are not just exploring AI, but but taking their projects to the next level.
Rocket speed for AI at Lockheed Martin
At Lockheed Martin, the company’s Data Analytics Innovations (DAI) Group uses AI-based predictive maintenance models to improve the availability of aircraft, helicopters and other equipment. The models accurately predict when technicians need to take a part out of service for maintenance, instead of relying on reactive approaches that perform maintenance on grounded aircraft.
Initially the DAI Group relied on traditional CPU-based systems to conduct its prognostics management. Increasing amounts of sensor data and a desire to use larger neural networks and models with more parameters led the group to install an NVIDIA DGX Station and two NVIDIA DGX servers, which are purpose-built to meet the demands of enterprise AI and data science. With no changes to the architecture or code, the group immediately experienced a 2x acceleration in training time. And with greater ability to train an tune parameters on their DGX system, they achieved a 10% boost in the accuracy of the algorithms overnight. In addition, the company saw an increase of 18x in speed toward training a few millions documents compared to CPU-based systems (read the full case study).
Continue reading: https://www.cio.com/article/309477/how-enterprises-boosted-ai-through-infrastructure-upgrades.html
Here are some recent examples of companies that are not just exploring AI, but but taking their projects to the next level.
Rocket speed for AI at Lockheed Martin
At Lockheed Martin, the company’s Data Analytics Innovations (DAI) Group uses AI-based predictive maintenance models to improve the availability of aircraft, helicopters and other equipment. The models accurately predict when technicians need to take a part out of service for maintenance, instead of relying on reactive approaches that perform maintenance on grounded aircraft.
Initially the DAI Group relied on traditional CPU-based systems to conduct its prognostics management. Increasing amounts of sensor data and a desire to use larger neural networks and models with more parameters led the group to install an NVIDIA DGX Station and two NVIDIA DGX servers, which are purpose-built to meet the demands of enterprise AI and data science. With no changes to the architecture or code, the group immediately experienced a 2x acceleration in training time. And with greater ability to train an tune parameters on their DGX system, they achieved a 10% boost in the accuracy of the algorithms overnight. In addition, the company saw an increase of 18x in speed toward training a few millions documents compared to CPU-based systems (read the full case study).
Continue reading: https://www.cio.com/article/309477/how-enterprises-boosted-ai-through-infrastructure-upgrades.html
