Abstract: New manufacturing techniques like 3D printing are under development, and they need monitoring methods to ensure the quality of the manufactured parts. Artificial Intelligence has outperformed traditional methods in the monitoring process and has shown high potential in recent years. New approaches in Artificial Intelligence, particularly Neural Architecture Search (NAS), have unlocked the potential for automated design of high-performance and resource-efficient deep learning models. In this work, we propose a training-based, low-fidelity NAS framework to systematically discover optimal architectures for regression tasks. Leveraging 8,610 candidate topologies, we trained models on only of the data for 10 epochs, enabling faster execution and selection of the architecture using low-fidelity information. The dataset belongs to Laser Powder Bed Fusion (LPBF), which is a manufacturing method that is still not well mastered and requires many trials before obtaining a satisfactory result. To cope with this issue, we developed a NAS algorithm to design a lightweight AI model (an architecture with a low number of parameters) to predict the process parameters from video information to ensure having the same printing parameters in action. The ultimate goal is then to embed the AI model in a low-latency feedback control loop that enables on-the-fly supervision of the printing process. The final designed architecture is based on 3-dimensional convolutional neural networks. The final AI models are 3–30 times lighter than off-the-shelf ones, while maintaining almost the same accuracy. This shows the potential of our methods when dealing with regression tasks in an industrial case study.