Much of ML in use within companies falls under supervised learning, which means proper training data (or labeled examples) are essential. The rise of deep learning has made this even more pronounced, as many modern neural network architectures rely on large amounts of training data. Issues pertaining to data security, privacy and governance persist and are not necessarily unique to ML applications. But the hunger for large amounts of training data, the advent of new regulations like GDPR, and the importance of managing risk means a stronger emphasis on reproducibility and data lineage are very much needed.
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