Many organizations want to integrate AI into their product or into their internal processes, but without sacrificing data privacy. For such organizations, the solution is to download and deploy AI models on their own servers instead of sending their data to the cloud. I am going to discuss this on-premise strategy in this article (also known as “edge AI”).
What Does On-Premise / Edge Computing Mean?
On-premise or edge computing refers to the practice of processing and storing data closer to its source, rather than sending it to a centralized cloud infrastructure. In this approach, computing resources are located near the systems sending the data.
In other words, on-premise and edge computing are trendy expressions that describe the fact that an application is deployed on your own servers rather than using an external cloud service like a SaaS API.
2 scenarios can be considered as on-premise: either you have your own machines hosted in your own facilities, or you leverage a cloud vendor like AWS, GCP, Azure… Strictly speaking the latter is less “on-premise” because you do not have control over the underlying server, but in general both can be considered as valid on-premise / edge solutions.
Why Is On-Premise AI / Edge AI Important?
On-premise or edge computing offers several advantages. Firstly, on-premise or edge computing dramatically enhances data privacy and security by keeping sensitive information closer to the source, reducing the risk of unauthorized access or data breaches during transportation to the cloud, and preventing cloud actors from leveraging your data for unwanted purposes. It also helps organizations comply with data regulations and laws that require local storage and processing.
Furthermore it reduces latency as data does not have to travel long distances to reach the cloud, allowing for faster processing and real-time analysis. Additionally, it minimizes dependence on network connectivity, ensuring operations can continue even when the internet is unreliable or disrupted.
AI is a very good on-premise candidate.
The first reason is that organizations tend to send extremely sensitive data to AI models. This is especially true in critical fields like medical applications, financial applications,… But not only.
The second reason is that AI actors on the market today tend to reuse customers’ data for their own business. OpenAI is a good example: for instance when organizations send data to ChatGPT, the data is scrutinized and OpenAI can reuse your data to train their own AI models. ChatGPT and GPT-4 privacy concerns are central questions that lead many organizations to focus on on-premise strategies.
How To Deploy AI Models On-Premise / At The Edge?
Deploying AI models on-premise involves setting up the infrastructure to host, manage, and serve the AI model within an organization’s own data center or managed infrastructure, rather than in the cloud.
Here are some common steps involved in the deployment of an AI model on-premise:
- Select the right AI model for the task.
- Evaluate hardware requirements such as CPU, GPU, and memory needs, based on the complexity and size of the AI model.
- Set up the required deep learning frameworks (TensorFlow, PyTorch, etc.) and other libraries.
- Convert the AI model to a format that is optimized for deployment (ONNX, TensorFlow Lite, OpenVINO, etc.), if not already in a deployable state.
- Perform model quantization or pruning to optimize for inference speed and memory usage, if necessary.
- Containerize the application and dependencies, which makes it easier to manage and scale.
- Develop or adapt an inference application to handle incoming requests to the AI model. This service typically includes APIs that allow users to interact with the model to get predictions.
On-premise / Edge Computing VS Cloud Computing: Pros And Cons
On-premise or edge computing has limitations. The computing resources available at the edge are typically limited compared to cloud infrastructure, which may restrict the complexity of applications that can be deployed. Additionally, maintaining and managing distributed computing resources across multiple locations can be challenging, requiring additional investments in IT infrastructure and expertise.
In general, such a strategy is more costly than relying on a managed SaaS offering like OpenAI, Anthropic, NLP Cloud…
Last of all, data privacy is only guaranteed if the underlying on-premise infrastructure is correctly secured.
Conclusion
On-premise AI / edge AI is skyrocketing now that AI is gradually gaining traction among organizations.
Such a trend is understandable: AI is used in all sorts of critical applications that have strong privacy requirements and - by design - standard cloud actors cannot meet these requirements.