Introduction to SageMaker Data Wrangler
Data processing is an integral part of machine learning (ML). In fact, it is not a stretch to say that ML models are only as good as the data that is used to train them. According to a Forbes survey from 2016, 80% of the time spent on an ML engineering project is data preparation. That is an astonishingly high percentage of time. Why is that the case? Due to the inherent characteristics of data in the real world, data preparation is both tedious and resource intensive. This real-world data is often referred to as dirty, unclean, noisy, or raw data in ML. In almost all cases, this is the type of data you begin your ML process with. Even in rare scenarios where you think you have good data, you still need to ensure that it is in the right format and scale it to be useful. Applying ML algorithms on this raw data would not give quality results as they would fail to identify patterns, detect anomalies correctly, or generalize well enough outside their...