Hutom is a big data platform that helps hospitals and surgeons provide more optimized patient care by leveraging machine learning and computer vision. The platform's automated system assists in better personalized surgical planning, real-time surgeon assistance, surgery analysis and review for learning and archival purposes. However, training deep learning models on medical data poses several technical challenges such as acquiring medical data due to legal or administrative requirements, high cost of annotation, small non-diverse datasets, and performance gains from scaling up computing resources. To overcome these challenges, Hutom utilized a synthetic data generation technique, implemented domain randomization and semi-supervised methods, and leveraged Ray and its ecosystem for distributed training and hyperparameter search. The platform's use of Ray Tune, which is a hyperparameter tuning library built on Ray, enables easy launch of multi-node distributed hyperparameter sweeps with features such as automatic management of checkpoints and logging to TensorBoard. Additionally, the platform uses Ray Train, a lightweight library for distributed training of deep learning models, to enable distributed training on GPUs. After obtaining the best performing model from hyperparameter search, the model is deployed using Ray Serve, which enables easy scaling of surgical video analysis, review, and deployment. The deployment utilizes Ray Serve's batching feature to perform computation in parallel, resulting in a 29% reduction in latency by increasing batch size.