Developed the Moving MNIST Study Experiment Infrastructure for the lanl/Yoke repository, focusing on enabling reproducible and automated machine learning experiments. The work centered on building a harness infrastructure and implementing batch-script driven pipelines, allowing for streamlined experiment setup, execution, and logging. By introducing configuration-based experiment management, the solution reduced manual intervention and accelerated the process of training and evaluating model variants. Python scripting and Bash were used extensively to support batch processing and data science workflows. Over the course of the month, the contribution delivered a single feature, emphasizing robust automation and reproducibility without addressing any major bug fixes.