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 specific industries, including automotive, healthcare, financial services, and manufacturing.
I’ll be starting with the automotive industry, exploring how companies are applying the data engineering and data science technologies I’ve been discussing to transform transportation. I’ll take a closer look at the problems companies are trying to solve, and explore approaches for gathering data from a variety of sensors and other sources as well as building appropriate data pipelines to satisfy both training and inferencing needs.
Even when you focus on a single industry like automotive, the number of possible AI use cases is large. NetApp divides AI in the auto industry into four segments with multiple use cases in each segment:
Naturally, there are overlaps between some of these segments; success in one area can yield benefits in another. For example, autonomous driving may be an essential element of a mobility-as-a-service strategy. There are also many requirements that all segments have in common, including infrastructure integration, advanced data management, and security/privacy/compliance.
I’ll look at each of these segments in more detail in coming blogs, but I want to introduce them here, and highlight some of the key challenges and use cases in each.
When you think about AI in automotive, self-driving is likely the first use case that comes to mind. While the holy grail in the industry is full self-driving, most companies are already offering increasingly sophisticated adaptive driver assistance systems (ADAS) as stepping stones toward Level 5 autonomy.Figure 1: The five stages of autonomy[/caption]
But the challenges to achieving full self-driving are significant. Each car deployed for R&D generates a mountain of data (1TB per hour per car is typical). Teams can expect to accumulate hundreds of petabytes to exabytes of data as autonomous driving projects progress, resulting in significant challenges:
I’ll cover many of these autonomous driving topics in-depth in the next several blogs, including architecting data pipelines for gathering and managing data, DL workflows, and the various models that researchers are exploring to achieve autonomous driving.
We increasingly expect all our devices to be connected and intelligent like our smart phones. Cars and other vehicles are quickly transforming into connected devices, and there are a number of immediate use cases for AI in connected cars.
Today, cars use cellular and WiFi connections to upload and download entertainment, navigation, and operational data. In the near future, we’ll also see cars connecting to each other, to our homes, and to infrastructure. Audi has already introduced technology to connect cars to stoplight infrastructure, enabling drivers in select cities to catch a “green wave”, timing their drives to avoid red lights.
That’s just one of many opportunities to use data from connected cars. While not every use case requires artificial intelligence, in an upcoming blog I’ll focus on several important use cases that do, including predictive maintenance. We’ll explore approaches to efficiently gather and process information from cars around the globe.
In the future, car ownership may decline in favor of various forms of ride sharing, particularly in dense urban areas. Car companies will need to become mobility companies to address changing consumer demand. Many car companies are already branching out, acquiring scooter- and bike-sharing companies and creating delivery services.
The machine learning and deep learning problems in mobility-as-a-service models are significantly different than those in autonomous driving:
From an infrastructure standpoint, these distributed problems require different strategies and may require smart algorithms on the consumer’s device (smart phone), in the vehicle, and in the cloud, plus long-term, secure data management for compliance.
The auto industry has a lot on its plate. Companies must look for ways to increase operational efficiency to free up capital for investments like those described above. Industrial Internet of Things (IIoT) and Industry 4.0 technologies are the key to streamlining business, automating and optimizing manufacturing processes, and increasing the efficiency of the supply chain.
Common manufacturing use cases include:
I’ll explore the applications of AI for smart manufacturing across all industries, including automotive, in a future blog.
NetApp is an exhibitor at TU-Automotive Detroit, the world's largest auto tech conference and the only place to meet the most innovative minds in connected cars, mobility & autonomous vehicles under one roof. Come to our booth C224 to meet with our auto subject matter experts.
Learn about how NetApp is partnering with NVIDIA, systems integrators, hardware providers and cloud partners to put together smart, powerful, trusted AI automotive solutions to help you achieve your business goals.
Attend the panel discussion: AI & the Brains Behind the Operation on June 6, 2:45 pm, with Thomas Carmody, Head of Transport and Infrastructure at our partner Cambridge Consultants (booth B140). Thomas will be addressing—amongst other topics—how to anticipate data storage challenges to meet autonomous vehicles (AV) grade level requirements.
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Previous blogs in this series:
Santosh Rao is a Senior Technical Director and leads the AI & Data Engineering Full Stack Platform at NetApp. In this role, he is responsible for the technology architecture, execution and overall NetApp AI business. Santosh previously led the Data ONTAP technology innovation agenda for workloads and solutions ranging from NoSQL, big data, virtualization, enterprise apps and other 2nd and 3rd platform workloads. He has held a number of roles within NetApp and led the original ground up development of clustered ONTAP SAN for NetApp as well as a number of follow-on ONTAP SAN products for data migration, mobility, protection, virtualization, SLO management, app integration and all-flash SAN. Prior to joining NetApp, Santosh was a Master Technologist for HP and led the development of a number of storage and operating system technologies for HP, including development of their early generation products for a variety of storage and OS technologies.
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