Introduction to edge AI
Last updated
Last updated
Edge AI is the process of running artificial intelligence (AI) algorithms on devices at the edge of the Internet or other networks. The traditional approach to AI and machine learning (ML) is to use powerful, cloud-based servers to perform model training as well as inference (prediction serving). While edge devices might have limited resources compared to their cloud-based cousins, they offer reduced bandwidth usage, lower latency, and additional data privacy.
The following series of articles and videos will guide you through the various concepts and techniques that make up edge AI. We will also present a few case studies that demonstrate how edge AI is being used to solve real-world problems. We encourage you to work through each video and reading section.
You will find a quiz at the end of each written section to test your knowledge. At the end of the course, you will find a comprehensive test. If you pass it with a score of at least 80%, you will be sent a digital certificate showing your completion of the course. You may take the test as many times as you like.
This series can be viewed as a course. We will cover the following concepts with the given learning objectives:
Understand the differences between cloud and edge computing
Advantages and disadvantages of processing data on edge devices
What is the Internet of Things (IoT)
What are the differences between artificial intelligence, machine learning, and deep learning
Understand the history of AI
What are the different categories of machine learning, and what problems do they tackle
Articulate the difference between training and inference
How does traditional cloud-based AI inference work
What are the benefits of running AI algorithms on edge devices
Examples of edge AI systems
What are the business implications for future edge AI growth
Define and provide examples for the different edge computing devices
How to choose a particular edge computing device for your edge AI application
How to identify a use case where edge AI can uniquely solve a problem
Identify constraints to edge AI implementations
Understand the edge AI pipeline of collecting data, analyzing the data, feature engineering, training a model, testing the model, deploying the model, and monitoring the model's performance
Identify the three principles of MLOps: version control, automation, governance
Describe the benefits of automating various parts of the edge AI lifecycle
Define operations and maintenance (O&M)
How does edge MLOps differ from cloud-based MLOps
Define the causes of model drift: data drift and concept drift
How does a short learning curve lead to faster go-to-market times
Articulate the advantages and disadvantages of using an edge AI platform versus building one from scratch
Case study: Tunstall Healthcare - Coming soon!
How is edge AI being used to improve existing fall detection technology
Why does reducing false positives and false negatives reduce costs and save lives
How is edge AI used to improve healthcare technology beyond fall detection
How is edge AI used to detect anomalies on power lines
How anomaly detection on edge devices saves power over cloud-based approaches
Resources to dive deeper into the technology and use cases of edge AI
How to get started with Edge Impulse
Comprehensive test and certification
Edge computing is a strategy where data is processed and stored at the periphery of a computer network. In most cases, processing and storing data on remote servers, especially internet servers, is known as "cloud computing." The edge includes all computing devices not part of the cloud.
Edge computing devices includes personal computers, smartphones, IoT devices, home and enterprise routing equipment, and remote or regional servers. As these devices become more powerful, we can start to run various AI algorithms on them, which opens up new ways to solve problems.
Practice your understanding with the quiz below. Submit your answer and click View accuracy to see your score. Note that this will open a new browser tab.
In the , we will dive into the advantages and disadvantages of edge computing.
