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Building ML models on the Edge using Wallaroo

Building ML models on the Edge using Wallaroo | Wallaroo.AI

When it comes to the ability to process data locally, ML on edge devices is a critical component. Deploying ML models on an edge device means models can be deployed in real-time and more instantaneously than they could have been otherwise. According to recent research, the global market size for Edge AI Software will amount to US$ 1087.7 million by 2024. This demonstrates that enterprises are rapidly embracing the adoption of ML applications on Edge models. 

Machine Learning on the Edge is a powerful approach for building ML models. It is becoming increasingly important for companies to be able to process data at the edge of their network. Being closer to the devices where the data is generated can be advantageous for a variety of reasons including latency and lower cloud/CDN costs.

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Advantages to Edge Machine Learning

ML on edge devices extends the reach of ML all the way to the point of production, where the data we seek to leverage is generated. Its application within businesses and enterprises has been made most popular from several advantages it provides: 

Low Latency

Latency issues often occur due to slow transfer rates between devices and infrastructures. By having your ML models on edge devices, data sets are processed locally, which means that less data will be transferred over to the cloud on average, excluding extenuating circumstances. ML on the edge presents the benefit of low latency in real-time applications, allowing for more immediate processing of data. 

Limited Connectivity & Offline Execution

ML on the edge also allows for the deployment of models in air-gapped environments, in instances where there is limited connectivity. It allows for real-time analytics during applications in offline environments where there is no requirement or availability for internet access during execution. This is particularly useful for enterprises running in remote areas, ensuring that the model is always available even in a closed circuit environment. 

Privacy

For companies that value privacy, running  ML on edge devices allows for the deployment of models without risking the exposure of any training data used, process results, or trade secrets as the data is transferred to a server. However, the metadata on the model performance can still be backed up selectively as required. 

Reduced Costs

Running ML on the edge can lower costs as well for companies looking to benefit from ML. Since it can reduce the cost of transferring large amounts of data over long distances, as well as the cost of bandwidth many businesses reap the benefits from this as well. 

Building & Deploying ML Models on the edge with Wallaroo 

Not only does Wallaroo have a lightweight stack for creating ML standalone solutions, but it also allows for the deployment, monitoring, and observability of ML models on edge devices. Below is a brief guideline for deploying ML models on the edge using the Wallaroo: 

  • Model Creation: Start by creating your model using any library, framework, or model deployment platform such as TensorFlow, Vertex.ai, PyTorch, DataBricks, or DataRobot, among others.
  • Model Deployment: Proceed to deploy your model using SDK, API, or UI.
  • Create Pipelines: Once the model is deployed, develop the central pipelines required to manage logins and learning. You can also conduct A/B testing and other experiments within your edge environment.  
  • Wallaroo Model Registry: To monitor your model’s performance, observe any issues, and update the model as required, the Wallaroo Model Registry allows for the deployment, monitoring, and versioning of your models. 
  • Model Edge Platform: Wallaroo allows for on-premises data aggregation and edge device management through the Model Edge data platform. It manages model deployment to all devices and handles model insights to and from the model registry.
  • Wallaroo Edge Inference: Whether connected or air-gapped, the constrained edge devices collect data and run model predictions through the Wallaroo Edge Inference. The details will then be sent back to the Model Edge data platform, including the telemetry metrics and the logins. 

When it comes to building and deploying ML models on the edge, Wallaroo is the ultimate choice. Read our Edge machine learning deployment architecture on Wallaroo blog to learn more about the Wallaroo ML on Edge features and reach out to us if you are interested in running a proof of concept for edge machine learning using Wallaroo.

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