Last time I talked about how automotive companies are using AI for a wide variety of use cases from autonomous vehicles to connected cars to mobility as a service. This time I want to dig into ways to leverage the data engineering and data science technologies I’ve been discussing to solve autonomous driving challenges. […]
So far in this blog series, I’ve focused on the nuts and bolts of planning AI deployments, building data pipelines from edge to core to cloud, and the considerations for moving machine learning and deep learning projects from prototype to production. Over the next several months, I want to focus on real-world AI use cases […]
In my previous post, I explained how NetApp and NVIDIA bridge the CPU and GPU universes, enabling you to tackle a wide variety of computing problems more efficiently: NVIDIA GPU computing helps you significantly reduce server sprawl, and NetApp helps you reduce data sprawl by coupling flash, NVMe-oF, and SCM acceleration with intelligent data management, […]
Data center technology moves in cycles. In the current cycle, standard compute servers have largely replaced specialized infrastructure. This holds true in both the enterprise and the public cloud. Although this standardization has had tremendous benefits, enabling infrastructure and applications to be deployed more quickly and efficiently, the latest computing challenges threaten the status […]
In my previous blog, I talked about the significant advantages that ONTAP AI provides for anyone that wants to deploy AI infrastructure quickly and get results fast. ONTAP AI combines: NVIDIA DGX-1 AI supercomputers NVIDIA’s fully optimized deep learning software stack NetApp’s AFF A800 for breakthrough I/O performance NetApp Data Fabric for edge to core […]
In this blog series, I’ve focused on how NetApp can help you streamline your artificial intelligence projects. With technologies and services for managing data everywhere, NetApp is well positioned to solve your AI data challenges. With the announcement of NetApp® ONTAP® AI, we’re taking our commitment to your AI success a step further. Built […]
At this year’s GPU Technology Conference (GTC) in San Jose, NVIDIA announced its second-generation artificial intelligence (AI) system with 10x the performance of the first generation. This breakthrough in GPU performance opens up a whole new range of use cases in the deep learning and data science worlds, and it also underscores the critical need […]
In previous blogs in this series, I talked about the edge-to-core-to-cloud architecture and the fact that in a data pipeline there are different I/O characteristics for data flowing in from the edge versus data flowing from the data lake into the training cluster. In this blog, I want to go one level deeper and talk […]
In my previous post in this series, I talked about approaches for eliminating I/O bottlenecks across your entire deep learning pipeline whether the core of your pipeline is on-premises or in the cloud. This time I want to talk more specifically about evolving approaches to infrastructure and data management that can help you cope […]
In the first post in this blog series, I talked about the challenges of operationalizing deep learning and other AI pipelines. These challenges can include I/O performance bottlenecks, as well as issues with moving, copying, and managing growing datasets. Whether you execute your AI workflow on premises or in the cloud, operational bottlenecks can […]
Enterprises are eager to take advantage of artificial intelligence (AI) technologies as a means to introduce new services and enhance insights from company data. However, as data science teams move past proof of concept and begin to operationalize deep learning, many are experiencing issues with data management. They may struggle to deliver the necessary performance, […]
Santosh Rao