Data validation and review
Data validation is essential for ensuring the quality of not only the training data, but also the performance of any AI solution.
It is arguably the most important step in machine learning. Without proper validation, poor data can infiltrate your model, corrupting the entire process. For that reason, exhaustive QA checks are crucial, not only for accuracy, but also for relevance, appropriateness and proper optimization.
All file formats
Data collection and generation
Data annotation and labelling