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It has always been the case that garbage in is garbage out in computing, but 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.
- The market for AI and machine learning relevant data preparation solutions is over $500M in 2018 growing to $1.2B by end of 2023.
- Data preparation and engineering tasks represent over 80% of the time consumed in most AI and Machine Learning projects.
- The market for third-party Data Labeling solutions is $150M in 2018 growing to over $1B by 2023.
- For every 1x dollar spent on Third-Party Data Labeling, 5x dollars are spent on internal data labeling efforts, over $750M in 2018, growing to over $2B by end of 2023.
- For every 1x dollar spent on Third-Party Data Labeling solutions, 2x dollars are spent on internal data efforts to support or enhance those labeling efforts.
- AI projects relating to object / image recognition, autonomous vehicles, and text and image annotation are the most common workloads for data labeling efforts.
- Within the next two years, all competitive data preparation tools will have machine learning augmented intelligence as a core part of the offering
- The human in the loop is not going away any time soon for data labeling and AI quality control.
Key Vendors Included in this Report:
- Figure Eight
- Melissa Data
- 24 Pages
- 14 Charts