Document ID: CGR-DLP20 | Last Updated: Jan. 31, 2020
Garbage in is garbage out in computing, and it is especially the case with regards to machine learning data. In this report, Cognilytica evaluates the requirements for data preparation solutions that aim to clean, augment, and otherwise enhance data for machine learning purposes, data engineering solutions that aim to give organizations a way to move and handle large volumes of data, and data labeling solutions that aim to augment data with the required annotations that are necessary to be used in machine learning training models. This report is an update to the previous research (CGR-DE100) concluded in 2019 with new data, vendors, and updates to market sizing and segmentation.
Key Vendors Included in this Report:
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