What you’ll learn:
- AI-at-the-edge data is processed locally, which is advantageous for real-time applications or scenarios when low latency is critical.
- A key benefit of edge AI is improved privacy and security; sensitive information is kept on the device and only the inferences, or metadata, are sent to the cloud.
- How TDK SensEI’s AutoML platform democratizes AI development to gain actionable insights from sensor data
As industry drives toward digital transformation (DX), one of the key elements that’s needed is data to sense what’s happening. With the advent of the industrial Internet of Things (IIoT), medical IoT (MIoT), smart homes, smart wearables, and other smart devices, it’s all about sensors.
Alongside the sensors, these devices integrate a microcontroller to perform localized artificial intelligence (AI) at the edge to understand the data and provide actionable insights, as well as a battery and connectivity to communicate the sensing data. As this technology ramps up, myths and misconceptions about it also begin to emerge. TDK SensEI’s Michael Johnston sets the record straight:
1. Edge ML is difficult to develop and requires expensive engineering resources.
In the past, developing machine-learning (ML) solutions required expensive engineering resources—domain experts who understand the application and AI experts who design ML algorithms and models that can learn from the data and make actionable predictions or decisions. Today, automated ML applications like AutoML are helping businesses build and scale AI at the edge, enabling those with domain expertise, but not ML expertise, to solve real-world problems quickly and efficiently.
2. Edge ML runs on powerful, expensive hardware.
Edge ML runs models and algorithms on edge devices rather than relying on cloud-based servers. While it’s true that powerful and expensive hardware is sometimes necessary for data-intensive tasks such as image recognition, natural language processing, or video processing, tinyML doesn’t require expensive hardware to run. In fact, tinyML technologies are already running on low-cost, low-power devices worldwide.
3. Edge ML is only for big businesses.
Edge ML technology is becoming more and more affordable and accessible, and it has relevance and applicability in diverse applications. Indeed, edge ML allows for customization to specific requirements, which is particularly useful for smaller businesses that may have niche needs or want to differentiate their products or services. In particular, industries such as healthcare, agriculture, retail, smart homes, and environmental monitoring already use edge ML to improve their operations and efficiency.
4. Edge ML is too complex to implement.
While edge ML can indeed involve intricate processes and technologies, it’s important to recognize that it has become increasingly user-friendly to implement. With tools like AutoML by TDK SensEI, businesses can create, deploy, and scale edge ML applications quickly and easily.
5. Edge ML is not secure.
Though security concerns do exist in the context of edge ML, they’re not insurmountable. The raw data is processed locally at the sensor node and not in the cloud. This means that sensitive operating data is not exposed to the outside world.
Security should be a fundamental consideration from the outset of any edge ML project, and ongoing vigilance is necessary to maintain a high level of protection for data, models, and devices. By following best practices, utilizing secure development methodologies, and staying informed about the latest security threats and solutions, it’s possible to design and deploy secure edge ML systems.
6. Edge ML is only suitable for certain industries.
While edge ML can have particularly strong applications in specific industries, its potential extends to a wide range of sectors. Edge ML is valuable for quality control, predictive maintenance, and process optimization in manufacturing. It’s able to analyze sensor data from production lines and equipment in real-time to identify defects or anticipate maintenance needs.
In healthcare, smart wearables and various other medical sensing devices are used for remote patient monitoring, early disease detection, and personalized treatment recommendations. Furthermore, drones and sensors equipped with edge ML capabilities can provide real-time insights for farmers to help optimize crop management, monitor soil conditions, and identify plant diseases.
7. Edge ML is expensive.
The cost of edge ML solutions is decreasing exponentially. Though there can be upfront costs, it’s possible to design edge ML systems that provide a strong return on investment by optimizing development, hardware selection, and operational efficiency.
Today, businesses are able to implement edge ML solutions without breaking the bank. Combined software and hardware solutions mean businesses can quickly create and deploy ML solutions for a variety of use cases without the need for expensive embedded resources and complex deployments.
8. Edge ML is not necessary.
Whether edge ML is necessary depends on the specific requirements and constraints of an application. Not every use case requires edge ML, but it provides valuable benefits in terms of real-time processing, data privacy, offline operation, cost savings, and adaptability. As technology continues to advance, the use of edge ML is likely to expand to address a broader range of scenarios and challenges.
9. Edge ML is too new.
Edge ML is indeed a relatively new paradigm within the field of ML. However, it’s far from its infancy. The rapid advances, real-world use cases, and industry support demonstrate its maturity and readiness for practical applications across a wide range of industries, from industrial processing lines to consumer goods like toothbrushes. The technology continues to evolve and is expected to play an increasingly significant role in the future.
10. Edge ML is not mature enough for enterprise use.
Edge ML has indeed reached a level of maturity that suits it for a wide range of enterprise applications. Its ability to process data locally, ensure data privacy, and provide real-time insights positions it as a valuable tool for businesses looking to optimize operations, enhance decision-making, and gain a competitive edge in their respective industries.
Many hardware and software vendors like TDK offer enterprise-grade edge, industrial ML solutions that are designed to meet the needs of even the most demanding businesses.
11. Edge ML is only for specific applications.
Edge ML versatility allows it to be applied to a broad range of industries and application scenarios, including predictive maintenance, quality control, demand forecasting, fraud detection, and more. The key is to recognize the unique needs and advantages that edge ML can offer and tailor solutions to effectively meet those requirements.
The field of edge ML has witnessed significant growth and maturity in recent years, dispelling various myths associated with its adoption and application. As we navigate the digital transformation era, data and sensors play a pivotal role, making edge ML a key technology for various industries and use cases. We need platforms that effectively democratize AI development, empowering users to gain actionable insights from sensor data more efficiently.