Discussing the article: "Overcoming ONNX Integration Challenges"
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Check out the new article: Overcoming ONNX Integration Challenges.
ONNX is a great tool for integrating complex AI code between different platforms, it is a great tool that comes with some challenges that one must address to get the most out of it, In this article we discuss the common issues you might face and how to mitigate them.
ONNX (Open Neural Network Exchange) revolutionizes the way we make sophisticated AI-based mql5 programs. This new technology to MetaTrader 5 is the way forward to machine learning as it shows a lot of promise like no other for its purpose however, ONNX comes with a couple of challenges that can give you headaches if you have no clue how to solve them whatsoever.
If you deploy a simple AI technique like a feedforward neural network you might not be able to find the deployment process that problematic but since most real-life projects are much more complex you might be required to do a lot of things such as extracting time-series data, preprocess and transform the big data to reduce its dimensions not to mention when you have to use several models in one big project deploying ONNX models, in situations like this deploying ONNX can become complicated.
ONNX is a self-sufficient tool that comes with the ability to store an AI model only. It doesn't come with everything in the box necessary to run the trained models on the other end, it is up to you to figure out how are you going to deploy your final ONNX models. In this article, we will discuss the three challenges which are scaling & normalizing the data, introducing dimension reduction to the model and, overcoming the challenge of deploying ONNX models for time-series predictions.
This article assumes you have a basic understanding of machine learning and AI theory, and that you have at least tried to use ONNX models in mql5 once or twice.
Author: Omega J Msigwa