{"id":168652,"date":"2021-06-09T21:30:51","date_gmt":"2021-06-09T16:30:51","guid":{"rendered":"https:\/\/venturebeat.com\/?p=2695432"},"modified":"2021-06-09T21:30:51","modified_gmt":"2021-06-09T16:30:51","slug":"how-edge-ai-can-make-enterprises-more-agile","status":"publish","type":"post","link":"https:\/\/www.technologyforyou.org\/how-edge-ai-can-make-enterprises-more-agile\/","title":{"rendered":"How edge AI can make enterprises more agile"},"content":{"rendered":"
<\/div>\n
\n

Elevate your enterprise data technology and strategy at Transform 2021<\/a><\/em>. <\/p>\n

\n<\/div>\n

The pandemic has accelerated the adoption of edge computing, or computation and data storage that\u2019s located close to where it\u2019s needed. According to the Linux Foundation\u2019s State of the Edge report<\/a>, digital health care, manufacturing, and retail businesses are particularly likely to expand their use of edge computing by 2028. This is largely because of the technology\u2019s ability to improve response times and save bandwidth while enabling less constrained data analysis.<\/p>\n

While only about 10% of enterprise-generated data is currently created and processed outside a traditional datacenter or cloud, that\u2019s expected to increase to 75% by 2022, Gartner says<\/a>. Internet of things (IoT) devices alone are expected<\/a> to create over 175 zettabytes of data in 2025. And according<\/a> to Grand View Research, the global computing market is anticipated to be worth $61.14 billion by 2028.<\/p>\n

Edge computing represents a powerful paradigm shift, but it has the potential to become even more useful when combined with AI. \u201cEdge AI\u201d describes architectures in which AI models are processed locally, on devices at the edge of the network. As edge AI setups typically only require a microprocessor and sensors, not an internet connection, they can process data and make predictions in real time (or close to it).<\/p>\n

The business value of edge AI could be substantial. According<\/a> to Markets and Markets, the global edge AI software market is anticipated to grow from $590 million in 2020 to $1.83 billion by 2026. Deloitte estimates<\/a> more than 750 million edge AI chips that perform tasks on-device have been sold to date, representing $2.6 billion in revenue.<\/p>\n

What is edge AI?<\/h2>\n

Most AI processes are carried out using cloud-based datacenters that need substantial compute capacity. These expenses can add up quickly. According<\/a> to Amazon, the cost of inference \u2014 i.e., when a model makes predictions \u2014 constitutes up to 90% of machine learning infrastructure costs. And a recent<\/a> study conducted by RightScale found that cost savings was behind cloud initiatives in over 60% of organizations surveyed.<\/p>\n

Edge AI, by contrast, requires little to no cloud infrastructure beyond the initial development phase. A model might be trained in the cloud but deployed on an edge device, where it runs without (or mostly without) server infrastructure.<\/p>\n

As Machine Learning Quant notes<\/a>, edge AI hardware generally falls into one of three categories: (1) on-premise AI servers, (2) intelligent gateways, and (3) edge devices. AI servers are machines with specialized components designed to support a range of model inferencing and training applications. Gateways sit between edge devices, servers, and other elements of the network. And edge devices perform AI inference and training functions, although training can be delegated to the cloud with only inference performed on-device.<\/p>\n

The motivations for deploying AI hardware at the edge are myriad, but they often center around data transmission, storage, and privacy considerations. For example, in an industrial or manufacturing enterprise with thousands of sensors, it\u2019s usually not practical to send vast amounts of sensor data to the cloud, have the analytics carried out, and return the results to the manufacturing location. Sending that data would require huge amounts of bandwidth, as well as cloud storage, and could potentially expose sensitive information.<\/p>\n

Edge AI also opens the door to using connected devices and AI applications in environments where reliable internet might not be a given, like on a deep-sea drilling rig or research vessel. Its low latency also makes it well-suited to time-sensitive tasks, like predictive failure detection and smart shelf systems for retail using computer vision.<\/p>\n

Edge AI in practice<\/h3>\n

Practically, edge AI incorporates a sensor \u2014 for example, an accelerometer, gyrometer, or magnetometer \u2014 connected to a small microcontroller unit (MCU), Johan Malm, Ph.D. and specialist in numerical analysis at Imagimob, explains in a whitepaper. The MCU is loaded with a model that\u2019s been trained on typical scenarios the device will encounter. This is the learning part, which can be a nonstop process through which the device learns as it encounters new things.<\/p>\n

For example, some factories use sensors mounted on motors and other equipment configured to stream signals \u2014 based on temperature, vibration, and current \u2014 to an edge AI platform. Instead of sending the data to the cloud, the AI analyzes the data continuously and locally to make predictions for when a particular motor is about to fail.<\/p>\n

Another use case for edge AI and computer vision is automated optical inspection on manufacturing lines. In this case, assembled components are sent through an inspection station for automated visual analysis. A computer vision model detects missing or misaligned parts and delivers results to a real-time dashboard showing inspection status. Because the data can flow back into the cloud for further analysis, the model can be regularly retrained to reduce false positives.<\/p>\n

Establishing a virtuous cycle for retraining is an essential step in AI deployment. A clear majority of employees (87%) peg data quality issues as the reason their organizations failed to successfully implement AI and machine learning, according to a recent Alation report<\/a>. And 34% of respondents to a 2021 Rackspace survey<\/a> cited poor data quality as the reason for AI R&D failure.<\/p>\n

\u201cMany of our customers literally deploy hundreds of thousands of sensors \u2026 In these IoT scenarios, it\u2019s not just a matter of IoT \u2014 it\u2019s IoT plus intelligent processing so that machine learning can be applied to get insights that improve safety and efficiency,\u201d Amazon CTO Werner Vogels told VentureBeat in a previous interview<\/a>. \u201cThere\u2019s a lot of processing that happens in the cloud because most [AI model training] is very labor-intensive, but processing often happens at the edge. Massive, heavy compute will [have a place] in the cloud for model training and things like that. However, their workloads aren\u2019t real-time critical most of the time. For our real-time critical operations, models must be moved onto edge devices.\u201d<\/p>\n

Challenges<\/h2>\n

Edge AI offers advantages compared with cloud-based AI technologies, but it isn\u2019t without challenges. Keeping data locally means more locations to protect, with increased physical access allowing for different kinds of cyberattacks. (Some experts argue the decentralized nature of edge computing leads to increased<\/em> security.) Compute power is limited at the edge, which restricts the number of AI tasks that can be performed. And large complex models usually have to be simplified before they\u2019re deployed to edge AI hardware, in some cases reducing their accuracy.<\/p>\n

Fortunately, emerging hardware promises to alleviate some of the compute limitations at the edge. Startups Sima.ai<\/a>, AIStorm<\/a>, Hailo<\/a>, Esperanto Technologies<\/a>, Quadric<\/a>, Graphcore<\/a>, Xnor<\/a>, and Flex Logix<\/a> are developing chips customized for AI workloads \u2014 and they\u2019re far from the only ones. Mobileye, the Tel Aviv company Intel acquired for $15.3 billion<\/a> in March 2017, offers a computer vision processing solution for AVs in its EyeQ product line. And Baidu<\/a> last July unveiled Kunlun, a chip for edge computing on devices and in the cloud via datacenters.<\/p>\n

In addition to chips, there\u2019s an increasing number of development motherboards available, including Google\u2019s Edge TPU<\/a> and Nvidia\u2019s Jetson Nano<\/a>. Microsoft, Amazon, Intel, and Asus also offer hardware platforms for edge AI deployment, like Amazon\u2019s DeepLens<\/a> wireless video camera for deep learning.<\/p>\n

These are among the developments encouraging enterprises to forge ahead. According<\/a> to a 2019 report from Gartner, more than 50% of large organizations will deploy at least one edge computing use case to support IoT or immersive experiences by the end of 2021, up from less than 5% in 2019. The number of edge computing use cases will jump even further in the upcoming years, with Gartner predicting that more than half of large enterprises will have at least six edge computing use cases deployed by the end of 2023.<\/p>\n

Edge AI\u2019s benefits make its deployment a wise business decision for many organizations. Insight predicts<\/a> an average ROI of 5.7% from intelligent edge deployments over the next three years. In segments like automotive, health care, manufacturing, and even augmented reality, AI at the edge can reduce costs while supporting greater scalability, reliability, and speed.<\/p>\n

\n

VentureBeat<\/h3>\n

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact. Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:<\/p>\n