Training machine learning models is often time consuming and requires setting up and maintaining infrastructure. Although the fast-paced evolution of cloud has taken away a lot of the on-premise infrastructure pain-points, even then the heavy-lifting and efficient usage of machines with GPU instances can be challenging when training compute intensive models with large amount of training data. In this article we discuss an end-to-end computer vision (CV) training approach by exploring how machine learning (ML) practitioners can fine-tune their deep learning models by leveraging Amazon SageMaker, that provides a fully managed service for all the stages of ML lifecycle -- data labelling and preparation, model building, training and tuning, deployment in cloud and edge, and MLOps. Although this is a CV specific example, it is applicable for other large-scale deep learning use-cases as well. We explore the business use-case of a fashion clothing marketplace who would like to enrich their metadata from the images that their sellers upload to the platform, thus improving inventory organization and personalization for their buyers.