Machine Learning Engineering for Edge AI: Challenges and Best Practices

The tasks of machine learning engineering are to analyze large amounts of data and create models based on the results in order to make predictions in various areas of human activity: business, medicine, and industry. An ML engineer is engaged in training neural networks, designing analytical networks and services based on machine learning algorithms. Learn more about ML and edge AI in this article.

What Is Machine Learning Engineering?

Machine learning engineering is a field related to the development, integration, and support of machine learning systems. ML engineering relies on engineering principles to design, develop, and deploy ML models, software, and algorithms.

The focus of Machine Learning engineering research is on the development of efficient ML systems with the ability to scale, adapted to processing of solid data sets and generation of reliable predictions. This process can be divided into separate processes - preparing data, creating and training a model, deploying it, and tracking work.

ML engineering specialists must have knowledge in the field of computer science, mathematics, and statistics, as well as in a specialized field. In addition, the ML engineer draws on experience designing and developing leading AI systems based on advanced AI algorithms. Such systems are capable of learning, reasoning, and making decisions based on input data. In addition, among the skills of an ML engineer is the mandatory knowledge of the programming languages Python, and Java, as well as an understanding of libraries and machine learning environments such as TensorFlow, PyTorch, etc. It is desirable to have a minimum knowledge of distributed computing and big data processing environments.

What Is Edge AI?

Edge computing suggests that data is processed directly near the source, at the network periphery, without transferring information to the central information processing node. This practice reduces processing time and makes calculations more productive.

A technology based on the application of AI algorithms and models on peripheral devices (smartphones, sensors, cameras) is called edge AI. It provides information processing locally, without involving central servers and cloud services, which optimizes the decision-making process in real-time. This approach is aimed at eliminating delays in work and maintaining privacy and security. In recent years, the application of edge AI has been relevant in applications for autonomous vehicle control, robotics, and smart homes.

Challenges of Machine Learning Engineering for Edge AI

Embedding ML engineering into edge AI faces a number of challenges that engineers typically don't encounter when working on standard ML projects. What problems do engineers have to solve?

1. Limited resources. The computing power, memory, and storage capacity of peripheral devices are relatively small, which requires the adaptation of ML models to these project features.
2. Real-time processing. Often, AI edge applications process data in real-time. In such circumstances, ML models must match the minimum delays and high throughput.
3. Battery-powered peripherals, so low power consumption of ML models is highly desirable to maximize battery life.
4. Poor data quality due to the use of noise suppression algorithms can become a determining factor in the performance of models.
5. Due to the small volume, typical for peripheral device storage, the requirement for compact dimensions is put forward for models.
6. The development and management of models are often complicated by the lack of special tools and experience in such processes from an engineer.

Read also: AI-Powered Application Development Guide for Business Owners

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Machine Learning Best Practices

Experts with a background recommend to stick to the following rules when working with ML models for edge AI:

• The work should start only after obtaining full information about exact functioning of the AI peripheral application, business data requirements for work, and whether there are restrictions for the peripheral device;
• the correct choice of the ML model is important. A model that is optimized for resource constraints, real-time processing, and low power consumption is needed;
• optimization of the performance of the model while maintaining the accuracy of the work, which is achieved through compression and quantization;
• collect high-quality data that reflects device usage and limitations;
• train and test the ML model based on the selected information;
• monitor the performance of the ML model using data logging, and predictive maintenance of the model to detect possible problems;
• model unfolding through special tools adapted to AI;
• support and update the ML model. This will ensure that the model meets the requirements of the application and the limitations of the peripheral.

The combination of ML and edge AI technologies actualizes new challenges that engineers did not face before. The Software Development Hub team will take on the task of creating an efficient and productive ML model adapted to run on edge devices under the constraints and changing requirements. During the development process, we take into account security and privacy issues, offering a reliable and efficient product.

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