Edge AI for Firmware Engineer

This course contains the use of artificial intelligence.

Artificial intelligence tools were used to support parts of the course creation process. The instructional design, technical workflow, engineering explanations, examples, and final course content were reviewed, refined, and shaped by the instructor.

This course teaches firmware, embedded systems, and software engineers how to build a practical Edge AI workflow from raw time-series sensor data to a verified, deployment-ready model pipeline.

You will begin by learning how to prepare sensor data for machine learning. You will inspect time-series signals, identify missing samples, analyze ranges and drift, discover periodic patterns, and repair common data-quality problems. You will also learn why chronological training, validation, and test splits are essential for preventing data leakage. The course demonstrates how to normalize data correctly and convert continuous sensor readings into sliding windows that can be used for model training.

Next, you will build and train a compact Long Short-Term Memory (LSTM) model for sequence prediction. LSTM concepts such as memory, cell state, hidden state, and gates are explained in practical engineering terms. You will implement the model, create a training loop, select an optimizer and loss function, save the best checkpoint, and interpret training curves. Hands-on failure demonstrations will help you recognize overfitting, unstable training, inappropriate data shuffling, and other common problems.

After training, you will export the model from PyTorch to ONNX, a portable format commonly used for model deployment. You will inspect the exported computation graph and verify its input and output contract. You will compare PyTorch and ONNX predictions to confirm numerical parity, measure inference performance, and evaluate results using real-world metrics such as MAE, RMSE, MAPE, and R².

Finally, you will run the complete pipeline from a clean starting point and learn how each generated file contributes to the final workflow. You will adapt the pipeline to new sensor signals, tune important parameters, and investigate deployment risks such as scaler mismatch, data leakage, quantization effects, and distribution drift.

By the end of the course, you will understand how sensor data becomes an AI-powered decision on an embedded platform. You will have a repeatable workflow for preparing data, training an LSTM, exporting it to ONNX, validating its behavior, and preparing it for device-side inference in real Edge AI applications.