As the number of ML use cases increases and becomes more complex, a growing number of MLops businesses are making investments in edge ML in order to run ML models on devices such as smart cameras, IoT computing devices, mobile devices, and embedded systems.
According to a recent forecast made by ABI Research, a prominent global provider of market research for the technology industry, the market for edge machine learning enablement is anticipated to expand to more than $5 billion by the year 2027. According to Lian Jye Su, research director at ABI Research, the market is still in its “nascent stage,” but enterprises are already looking to a wide variety of platforms, tools, and solutions to assist them in overcoming the challenges associated with the implementation of edge machine learning applications.
The senior product manager for artificial intelligence and analytics at SAS, Lou Flynn, stated that “we are clearly seeing MLops enterprises boosting the use of edge ML.” ” The cloud is becoming increasingly popular among businesses of all kinds for a variety of reasons, but it is not appropriate for all types of workloads. Hence, firms in a wide variety of industries, such as aerospace, industrial, energy, and automotive, use cutting-edge AI to achieve a competitive edge in their respective markets.
The MLops team is enthusiastic about edge ML for the following five reasons:
1-The speed and power of edge devices have increased significantly:
According to Frederik Hvilshj, lead ML developer at datacentric computer vision company Encord, “we have seen numerous organizations focus on end-to-end procedures surrounding edge ML.” The two main reasons, he said, are that edge devices are becoming more powerful and model compression is becoming more effective, allowing more powerful models to be run at a higher speed, and that edge devices are typically located much closer to the data source, removing the need to move large volumes of data.
Together, they allow “high-performance models to be run on edge devices at close to real-time speed,” as he put it. In the past, “getting the high model throughput” necessitated the usage of GPUs hosted on centralized servers, but this required frequent data transfers, making the use case inconvenient.
2-Edge ML improves productivity:
Content analysis for efficiency gains is a golden opportunity in today’s distributed data ecosystem, according to Flynn.
Numerous data sources come from far away, he said, citing a warehouse, a solo sensor at a huge agricultural site, and even a CubeSat as part of a constellation of electro-optical imaging sensors. Using edge ML instead of waiting for data to reconcile in the cloud would be more efficient in each of these examples, the authors write.
3-Essential factors are bandwidth and cost reduction:
Domino Data Lab’s head of data science strategy, Kjell Carlsson, explained that “you need to run ML models on the edge due of physics (bandwidth restrictions, latency, and cost). IoT, as Carlsson put it, is not practical if data from all sensors must be sent to the cloud for processing.
When asked about the number of cameras and other sensors needed for a smart store, he responded, “The network in a supermarket would not sustain the high-definition streaming from a couple dozen cameras.” He also pointed out that the expense of data transport might be avoided if ML were executed locally.
Using edge ML, “for instance, a Fortune 500 manufacturing is continuously monitoring equipment to predict equipment failure and warn workers to probable difficulties,” he said. Domino is keeping an eye on over five thousand signals with one hundred fifty deep learning models using the MLops platform.
4-EdgeML is useful for scaling the right data:
According to Hvilshj, the true benefit of edge ML is that it allows you to grow your model inference with dispersed devices rather than investing in more powerful servers.
Now that we’ve gotten through the challenge of scaling inference, we can focus on the next step, which is gathering the appropriate data for the next training cycle. While gathering raw data is usually straightforward, deciding which records to categorize next can be challenging when dealing with massive datasets. What might be more important to label can be determined with the aid of compute resources on edge devices.
He gave the following example: “If the edge device is a phone, and the user of the phone dismisses a prediction, this can be a good indicator that the model was wrong.” To retrain the model with accurate labels, “the specific piece of data would be useful.”
5-Companies using MLops would benefit from greater leeway:
According to Flynn, MLops organizations should use their models to do more than just make better decisions; they should also use their models to optimize these decisions for different hardware profiles, such as by compiling models to run more efficiently on different cloud providers and across devices with varying hardware (CPU, GPU, and/or FPGAs), using technologies like the Apache TVM (Tensor Virtual Machine). Georgia-Pacific, an American pulp and paper firm and one of SAS’s customers, employs edge computing at several of its remote industrial plants because high-speed connectivity is typically unreliable and expensive there.
When it comes to processing data, “this flexibility provides MLops teams agility to handle a wide variety of use cases,” Flynn explained. While the variety of devices is large, many of them have resource constraints that may prevent full model deployment. For this reason, model compression is essential. When a model is compressed, its size is reduced, allowing it to function on smaller devices (such as an edge device) without sacrificing computing performance.