AI infrastructure

Enterprises investing in deep learning platforms need AI infrastructure sufficient enough to synthesize a massive amount of data. Here, you'll find the information you need to make decisions about AI-specific compute architectures -- from GPU-packed servers to highly-scalable clustered computing systems built for big data and machine learning applications.

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August 13, 2018 13 Aug'18
O'Reilly machine learning survey: Enterprises aim for maturity

A new survey finds companies are using fairness and bias as metrics to evaluate the success of a machine learning model.
July 31, 2018 31 Jul'18
Google's Edge TPU breaks model inferencing out of the cloud

Google's Edge TPU is a force multiplier to compete against the likes of Amazon, IBM and Microsoft, and to attract next-gen app developers.
June 14, 2018 14 Jun'18
Quantum computing applications creep forward

Tech vendors and users are moving forward on quantum computing tools and research. While still futuristic, developments bear watching, Forrester's Brian Hopkins says.
May 18, 2018 18 May'18
New Intel toolkit OpenVINO supports deep learning on CPUs

A new developer kit from Intel seeks to lower the bar for doing deep learning on CPUs and other types of chips to extract more intelligence from video.

Bring yourself up to speed with our introductory content

GPU cloud tools take complexity out of machine learning infrastructure

While talk of AI on GPUs is abuzz, actually building a machine learning infrastructure remains a dark art. A startup's PaaS is looking to automate parts of the process. Continue Reading
GPU implementation is about more than deep learning

Simulmedia is using GPU technology to power reporting tools, while eyeing future deep learning applications, helping to justify the cost of the hardware while building experience. Continue Reading
Computer vision technology helps Trulia link buyers to homes

In this podcast, Trulia's vice president of engineering discusses the importance of computer vision applications to the website's overall goal of helping buyers find homes. Continue Reading

Weigh the pros and cons of technologies, products and projects you are considering.

Tech experts weigh in on the AI hype cycle

AI expectations couldn't be any higher. Read why leading industry experts believe the hype is deserved and what developers can do to deliver on the technology's weighty promise. Continue Reading
Enterprises explore AI voice assistant technology

Intelligent voice assistant devices, so popular among consumers, are starting to make their way into enterprises, but businesses need to be mindful of several challenges. Continue Reading
GPU databases bring greater parallelism to big data processing

GPU databases offer a new way to process data. 451 Research analyst James Curtis discusses where they fit in big data applications, particularly for parallel processing. Continue Reading

Learn to apply best practices and optimize your operations.

Machine learning in production challenges developers' skills

Deploying machine learning models requires an entirely different skill set than developing them, and data scientists and engineering teams need to be ready to bridge this gap. Continue Reading
Threats of AI include malicious use against enterprises

As sophisticated tools become easier to use, enterprises need to protect themselves against AI threats to ensure they do not become the victims of malicious attacks. Continue Reading
Artificial intelligence data storage planning best practices

AI storage planning is similar to the storage planning you're used to: Consider capacity, IOPS and reliability requirements for source data and the application's database. Continue Reading

We’ve gathered up expert advice and tips from professionals like you so that the answers you need are always available.

There's time and place for SMBs to adopt SaaS-based ERP

SMBs face many issues when implementing SaaS ERP operations versus on-premises, plus the fact that one size cloud-based ERP platform doesn't necessarily fit all companies. Continue Reading
Make room for AI applications in the data center architecture

For AI applications, the future is now. But implementing artificial intelligence in an enterprise data center presents obstacles for network, storage and compute infrastructures. Continue Reading