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.
- The market for AI and machine learning relevant data preparation solutions is over $1.5B in 2019 growing to $3.5B by the end of 2024.
- 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 $1.7B in 2019 growing to over $4.1B by 2024.
- An increasingly greater amount of workload requirements are becoming more domain-specific in nature as machine learning tasks become increasingly more specialized
- The increasing proliferation of pre-trained models and models-as-a-service will move an increasing amount of data labeling to automation over time or shift humans to more complex tasks,
- 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.
- By 2024, over30% of current labeling tasks will be automated or performed by AI systems. However, the human in the loop is not going away any time soonfor labeling and quality control.
Key Vendors Included in this Report:
- AI Data Innovations
- Altair Knowledge Works (formerly DataWatch)
- Amazon Mechanical Turk
- Amazon Sagemaker Ground Truth
- Clay Sciences
- Deep Vision Data
- Defined Crowd
- Figure Eight
- Gengo.ai (Acquired by Lionbridge)
- Hitachi Vantara (Pentaho)
- Mighty AI
- Paxata (Acquired by DataRobot)
- Q Analysts
- Scale AI
- 37 Pages
- 3 Tables
- 18 Charts
|Table of Contents
- Executive Summary 2
- Key Findings 2
- Market Overview 3
- Defining the Problem 3
- Data Engineering 4
- Data Preparation 5
- AI-Relevant Data Preparation Solution Requirements 6
- Data Preparation Use Cases 6
- Data Labeling 7
- Data Labeling and Annotation Use Cases 8
- Data Labeling Solution Provider Requirements 9
- Data Labeling Solution Provider Considerations 10
- Use of Automation and Machine Learning in Data Labeling 11
- Cognilytica Classification 13
- About the Cognilytica Vendor Classification System 13
- Market Size Estimates and Growth Projections 14
- Data Preparation Market Size and Growth Projections 14
- Data Labeling Market Size and Growth Projections 15
- Data Labeling Vendor Landscape 16
- Key Vendors 17
- Data Preparation Vendors 17
- Data Preparation Vendor Profiles 17
- Data Labeling Vendors 22
- Data Labeling Vendor Profiles 22
- Notes on Vendor Inclusion 33
- Future Market Trends and Predictions 34
- Data Preparation Market Predictions and Trends 34
- Data Labeling Market Predictions and Trends 34
- Related Research 35
- About Cognilytica 35
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