Accuracy is crucial when training computer vision models, resting on three core pillars: dataset quality, volume, and cleanliness; experimentation and training processes; and workflow, annotation tools, automation features, dashboard, quality control (QC), and quality assurance (QA) processes. Sourcing datasets for computer vision models can be done through using own data or open-source datasets, with a wealth of options available depending on the sector or use case. Data cleaning is essential to ensure clean data, which is necessary for successful experiments and training, as unclean data costs time and money. Cleaning images involves removing duplicate files, enhancing brightness and pixelation, while medical images require additional layers of file formats and scrubbing individual patient identifiers. Annotated datasets are critical, requiring diversity and quality to reduce bias and improve accuracy. Experiments are necessary to improve performance, improve the model, and gather data about its behavior, with failure being an inevitable part of the training process. Artificially-generated content can be used to test algorithms in different situations or scenarios where real-world examples may not be available. Improving computer vision model experiment workflows involves using tools that allow for quick experimentation and testing, such as Encord and Encord Active, which enable data ops managers to oversee annotation and training workflows more effectively and introduce data augmentation to reduce bias.