A novel, cost-effective development cycle using Amazon Braket is presented for training and evaluating hybrid quantum-classical algorithms. The process emphasizes reproducibility and efficient resource use, detailing ideation in Braket notebooks, scaling with Hybrid Jobs for hyperparameter optimization, and rigorous QPU verification. A variational quantum algorithm for image classification showcases data reduction, model training, and performance assessment with simulators and real quantum devices, offering practical insights for quantum machine learning projects.