How GenAI is changing edge computing (and the skills teams need to keep up)

This article was co-authored by Obinna Amalu and Kesha Williams. 

Generative AI (GenAI) has transformed the edge computing landscape, bringing powerful AI capabilities closer to where data is generated. This opens up new possibilities for real-time data processing, personalized experiences, and enhanced decision-making. 

In this article, we break down the essentials of GenAI and edge computing, explore how GenAI is revolutionizing the edge, and identify the skills you need to leverage this technology effectively.

GenAI refers to artificial intelligence systems capable of generating new content, such as text, images, or even music, based on the data they’ve been trained on. 

Unlike traditional AI models, which focus on prediction or classification tasks, GenAI creates new data that mimics the patterns and structures of its training inputs. Popular examples of GenAI include OpenAI's GPT-4, which can generate human-like text, and DALL-E, which can create images from text descriptions.

GenAI models typically rely on deep learning techniques like neural networks to understand and replicate complex data patterns. These models require extensive training on large datasets and substantial computational resources, but the results can be remarkably sophisticated and useful across various applications.

Traditional cloud-based systems send vast amounts of raw data to a centralized location for processing. Edge computing, also known as intelligent edge, uses distributed data processing and storage architecture to compute at or near the source of the data.

With edge computing, you bring computing and data sources spread across various devices, such as servers, gateways, smartphones, and IoT (Internet of Things) sensors, close to where data is consumed. In other words, you process data closer to where it’s generated.

This brings computation closer to the users and devices that need it, reducing latency and bandwidth usage while improving data processing efficiency, security, and real-time decision-making.

GenAI and edge computing are two distinct concepts that have evolved separately. Still, they’ve started to intersect in interesting ways. For example, edge platforms provide a stable and secure infrastructure to run GenAI applications.

Here are some other ways the intersection of edge computing and GenAI has transformed the world of technology.

With GenAI, edge devices (such as IoT sensors, cameras, or microcontrollers) can perform more sophisticated AI computations on site. Edge AI reduces the need for data transmission to a central server, minimizing latency and enhancing real-time decision-making.

Edge computing with GenAI enables devices to make decisions without relying solely on remote servers. This increased autonomy allows systems to respond faster and more effectively to changing conditions, improving overall efficiency and reliability.

By processing data locally, edge AI reduces the risk of data breaches or cyber attacks that might occur during transmission over public networks. GenAI-based edge devices can also detect anomalies and take corrective actions before the data is transmitted to a central server.

The combination of edge computing and GenAI enables faster analysis and interpretation of real-world data, providing actionable insights in near-real time. This has significant implications for industries such as manufacturing, transportation, and healthcare.

Edge AI with GenAI minimizes the time between data generation and processing, resulting in lower latency and improved responsiveness. This is particularly important for applications like autonomous vehicles, smart homes, or industrial automation.

As both edge computing and GenAI continue to evolve, we can expect even more innovative applications and transformations in various industries, especially manufacturing, retail, and healthcare.

GenAI and edge computing are two tech trends that have gained a lot of traction since the unveiling of ChatGPT. But the amount of resources needed to use these technologies have also increased in turn.

To be proficient in generative AI and edge computing, you and your teams will need these skills.

• Programming languages (Python or R)
• Math and data structures (linear algebra, calculus, probability theory)
• Deep learning frameworks (TensorFlow, PyTorch)
• Neural network architectures (CNNs, RNNs, transformers)

• Operating systems (Linux, Windows) and embedded systems
• Networking protocols (TCP/IP, HTTP/HTTPS)
• Device management and containerization
• Security best practices and data processing on edge devices
• Knowledge of edge AI frameworks (Ollama)

• Problem-solving and debugging
• Communication and collaboration
• Adaptability and lifelong learning
• Data analysis and interpretation
• Agile development methodologies

As companies start implementing a more modern approach to hybrid multi-cloud (HMC) and Modern Data Centers, edge computing emerges as a significant game changer in the infrastructure landscape. 

Hyperscalers like AWS, GCP, and Azure have invested heavily in providing solutions that cater to this need. Most recently, Google has pushed into this space with the release of the Google Distributed Cloud Connected/Hosted. 

With this service, a Google managed appliance sits on your enterprise network and provides a seamless integration to your existing cloud services. This comes in handy in industries that rely on almost real-time analytics and process massive amounts of data.

Generative AI is a breakthrough for edge computing, offering new capabilities and opportunities for innovation. By understanding the principles of GenAI and edge computing, acquiring the necessary skills, and staying informed about emerging trends, you can harness the full potential of this technology. 

If you’re just beginning with GenAI, learning prompt engineering is a great starting point.  Take a look at Kesha’s generative AI course Prompt Engineering for Improved Performance to master advanced techniques in prompt engineering!