Successful machine learning models are built on high-quality training datasets. Labeling raw data to get accurate training datasets involves a lot of time and effort because sophisticated models can require thousands of labeled examples to learn from, before they can produce good results. Typically, the task of labeling is distributed across a large number of humans, adding significant overhead and cost. Join us as we demonstrate Amazon SamurAI, a new capability of Amazon SageMaker that provides an effective solution to reduce this cost and complexity by combining a machine learning technique called active learning with human labeling. Active learning reduces the time and manual effort required to do data labeling, by continuously training machine learning algorithms based on labels from humans. By iterating through ambiguous data points, SamurAI improves the ability to automatically label data resulting in high-quality training datasets.

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