The ongoing improvement of AI models is crucial for adapting to dynamic environments, addressing model drift and errors, and enabling new services, features, and deeper personalization. This continuous learning ensures that AI solutions remain relevant and effective long after they are deployed.
The Problem with Edge Environments
Post-deployment training is uniquely challenging at the edge for several reasons, with resources being the primary constraint. While model training is a notoriously resource-heavy task, it must be executed on embedded systems without compromising performance. The inherent difficulty lies in the fact that, due to size constraints, embedded hardware often has limited compute, power, and memory.
Beyond resource limitations, data privacy and security present significant hurdles. Using data for training that has not been properly anonymized or encrypted can create a legal and regulatory minefield, especially when sensitive or personally identifiable information is involved.
Furthermore, edge environments do not always have reliable bandwidth. If a model relies on the internet for training, any connectivity interruption can disrupt its functionality.
While these challenges are technically complex, they can be overcome by adopting the right learning methods, training techniques, and technology stack.
How Edge AI Learns
Broadly speaking, post-deployment training at the edge is accomplished in one of three ways: on the device itself, in the cloud, or through a shared learning ecosystem. By pairing these methods with the right model adaptation techniques, organizations can ensure their AI continues to learn well after deployment.
On-Device Learning – With on-device learning, AI models are trained or updated directly on the edge hardware. The model is fine-tuned using data gathered from its own sensors or user interactions, which it then preprocesses and ingests.
This approach is well-suited for dynamic, real-time personalization and preserves user privacy by not transmitting or sharing data. The primary drawback, however, is the risk of overfitting; because local datasets are small, a model might "memorize" the data rather than learning a general pattern, rendering it unable to generalize to new data.
Federated Learning – As an alternative to purely on-device learning, federated learning introduces a central server to coordinate training across multiple devices. In this model, a central server shares an AI model with a fleet of edge devices. Each device then trains the model on its local dataset and transmits the results of its training—not the raw data—back to the server. The server aggregates these results and releases a consolidated model update to all devices.
Because only model updates are transmitted, this method is both bandwidth-efficient and secure. A key challenge, however, is that synchronizing these updates across diverse types of edge devices often requires specialized software.
Cloud-Based Retraining – The third approach, cloud-based retraining, is the most similar to traditional model training. This method involves a cloud platform collecting data from edge devices and transmitting it to the cloud to retrain a centralized AI model. Once training is complete, the updated model is transmitted back to each device.
While this approach can support more complex retraining tasks and comprehensive model updates, it is heavily reliant on connectivity and can be affected by latency. Furthermore, transmitting raw data raises privacy concerns, as sensitive information could be exposed without proper encryption.
Practical Techniques for Model Adaptation in the Field
To address the specific limitations of edge environments, several adaptation techniques can be paired with the learning methods described above.
- Incremental Learning: In incremental learning, the model assimilates new information as it arrives. This data is used primarily to fine-tune the model, manage drift, and adapt to new data patterns while the model retains its focus on its original function. A common example is a wearable fitness tracker being updated with new data about its wearer.
- Transfer Learning: Where incremental learning fine-tunes a model, transfer learning repurposes it. With this technique, a model that was pre-trained on a large, generic dataset is adapted for a specific task using a much smaller, local dataset. This dramatically reduces the compute, memory, and energy needed for model training, which in turn enables the deployment of more sophisticated AI at the edge. For instance, an autonomous driving system might use transfer learning to train its object detection model for a new environment.
- Continual Learning: Continual learning takes adaptation a step further, allowing a model to learn entirely new functions over time. Here, the model ingests a constant flow of data to help it learn new tasks or adapt to new capabilities. Unlike incremental learning, which focuses on the original task, continual learning expands the model’s domain, allowing it to better adapt to significant changes in its ecosystem. A smart thermostat, for example, could use continual learning to integrate with a newly installed smart air conditioner.
In practice, it is often most effective to adopt a hybrid approach that leverages multiple techniques, such as combining on-device training with continual learning and periodic cloud-based retraining.
The Importance of Finding the Right AI Stack
These advanced learning and adaptation techniques are powerful, but they depend on a well-thought-out hardware and software stack to ensure model updates can actually happen. This requires, first and foremost, hardware capable of supporting the model’s resource demands and software that can manage, coordinate, schedule, and secure training updates with minimal input.
Crucially, the hardware and software must function not as individual components, but as a unified whole. Without the right integrated infrastructure, a model cannot learn effectively after deployment and may run into critical bottlenecks, from resource shortages to outright hardware failure.
To solve this, SECO provides a full stack of integrated hardware, software, and tools designed for even the most complex edge AI deployments. Many of our edge devices feature dedicated Neural Processing Units (NPUs) and AI accelerators to handle demanding workloads, including support for third-party options like Axelera’s artificial intelligence processing unit (AIPU) hardware-based accelerators.
Tying this powerful hardware together is Clea, our modular software framework, which provides the complete ecosystem for post-deployment learning. We provide everything you need to first develop an AI model via the drag-and-drop Clea AI Studio, deploy it to one or more edge devices, and then ensure the model continues to learn, regardless of the edge device it runs on.
Final Thoughts
Post-deployment learning is essential for any successful edge AI implementation, as it allows a model to deliver better personalization, adapt to dynamic environments, and evolve with the needs of your business and customers. While choosing the right learning techniques is crucial for maintaining model accuracy and effectiveness, the underlying solution stack is just as important. SECO provides the integrated stack you need to succeed.
Contact us to discover how we can help you build adaptable, dynamic edge AI.