Machine learning is powering most of the recent advancements in artificial intelligence including autonomous systems, computer vision, natural language processing, predictive analytics, and a wide range of applications among the seven patterns of AI. However, in order for these systems to be able to create accurate generalizations, these machine learning systems must be trained on data. The more advanced forms of machine learning, especially deep learning neural networks, require significant amounts of data to be able to create models with acceptable levels of accuracy. If machine learning systems are going to learn from this data, then this data needs to be clean, accurate, complete, and well-labeled so the resulting machine learning models are accurate. Whereas it has always been the case that garbage in is garbage out in computing, it is especially the case with regards to machine learning data.
For companies looking to get started on their AI Data Engineering Lifecycle, we have put together this checklist to help.