Even in this new era of AI, the old computer science adage of “Garbage in, garbage out” remains as relevant today, if not more relevant, than ever before. Using data that is “ML model ready” is the difference between effective and ineffective AI implementation.
When it comes to training effective Machine Learning (ML) models, engineers are increasingly battling against messy data. This creates a challenge for those who are expected to make sense of and order these data sets for AI tools.
