How can machine learning model visualization help in edge computing?
Edge computing devices are designed for remote deployment and often have limited onboard resources. Companies are building machine learning models to increase the use of these systems for quick decision-making by analyzing the edge information. However, they often face challenges such as model efficiency and power consumption for battery-powered devices. These unique challenges and requirements necessitate a solution that can help companies optimize their models for these specific environments.
Machine learning model visualization can offer significant benefits, especially in the context of edge computing. Traditional model development tools often lacked the ability to provide a comprehensive understanding of the underlying data. However, Imagimob Studio‘s new GraphUX update changes this. It allows engineers to visualize their ML model workflow, enabling them to better understand patterns and distributions within the data. This, in turn, facilitates faster and more efficient development of edge device models.
“In traditional methods, the model is a black box, and there is no insight into what is going on inside the model. Graph UX provides that insight by visualizing the overall model, as well as giving a live view of the data as it is flowing through every part of the model,” says Alexander Samuelsson, CTO and co-founder at Imagimob in an exclusive interview with Edge Industry Review.
ML model visualization can significantly aid with model optimization and performance. It helps engineers understand the complexities of the model structure, how data flows through the model, and where transformation occurs. For instance, visualization can reveal how specific features, such as temperature or humidity readings, affect the output of a weather prediction model. It can also show how robust a model’s predictions are when faced with different types of data, such as varying sound frequencies in an audio recognition model.
Samuelsson explains, “Graph UX also makes models more robust and provides better explanations for models, as you can see more of what’s going on in them and more quickly identify problems. If we use the example of a model identifying coughing by listening to the environment in a healthcare setting, if there is a scenario where coughs are under-identified, you can see where the failure occurs and the data that it failed to classify, and then feed that back to better train the model.”
Beyond understanding the model structure, visualization can be a powerful tool for debugging models. It can help engineers identify specific issues affecting the model’s performance. For example, visualization could reveal that a model struggles to classify certain data types, such as low-frequency sounds in an audio recognition model. This insight can then diagnose errors in the model’s predictions, leading to more accurate and reliable results.
Also, the ability to view multiple models running in parallel can speed up the development process and compare and evaluate the models at the same time.
He adds: “Running multiple models in sequence is more power-efficient as you can use a lightweight model to trigger a larger model when needed. It also allows you to reuse models and save development time; for example, you can bring in an existing model that identifies sound features very accurately and run it alongside another model that builds on that model, perhaps by classifying a specific sound.”
As previously mentioned, these ML models have to be accurate and power efficient. However, if their accuracy decreases, they are not well suited for mission-critical industry applications, such as healthcare. With Graph UX, engineers can better explain models, see what’s going on in them, and more quickly identify problems.
“If we use the example of a model identifying coughing by listening to the environment in a healthcare setting if there is a scenario where coughs are under-identified, you can see where the failure occurs and the data that it failed to classify, and then feed that back to better train the model,” Samuelsson explains.
But Imagimob agrees that there’s more to be done with visualization of the model development process in edge applications. When asked about their future plans, Samuelsson says that they will include the ability for users to visualize and track various models and their performance throughout a project, offering greater control over model evaluation. The engineer can adjust evaluation metrics and create custom metrics to suit specific use cases.
“We will also bring data management and augmentation into Graph UX, which will give you more control over which data you use in different parts of your project. It will also allow you to combine and augment your data sources in a streamlined and flexible way. This allows you to develop your model so that it works in scenarios for which you don’t explicitly have the data,” Samuelsson concludes.
Article Topics
edge computing | Graph UX | Imagimob | machine learning | machine learning model
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