Data Preparation & Labeling for AI 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.
Premium PDF. Source: Copyright © Cognilytica LLC Key Vendors Included in this Report:
  • AI Data Innovations
  • Alegion
  • Altair Knowledge Works (formerly DataWatch)
  • Alteryx
  • Amazon Mechanical Turk
  • Amazon Sagemaker Ground Truth
  • Appen
  • CapeStart
  • Clay Sciences
  • ClickWorker
  • CloudFactory
  • CloverDX
  • Dataloop
  • DataMeer
  • Datapure
  • DataTurks
  • DataWatch
  • Deep Vision Data
  • Defined Crowd
  • Figure Eight
  • (Acquired by Lionbridge)
  • Heartex
  • Hitachi Vantara (Pentaho)
  • Hive
  • Hivemind
  • iMerit
  • Labelbox
  • Lionbridge
  • Melissa
  • Mighty AI
  • Paxata (Acquired by DataRobot)
  • Playment
  • Q Analysts
  • Samasource
  • Scale AI
  • Supahands
  • Superannotate
  • Supervisely
  • Talend
  • Tamr
  • Taskware
  • Trifacta
  • Welocalize
Report Details:
  • 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
Although Cognilytica believes that the results, conclusions, and analysis produced in support of this report are well informed, comprehensive, and reasonable, Cognilytica cannot guarantee future results, accuracy of market predictions, or applicability of conclusions to report purchaser or reader’s business. Moreover, Cognilytica does not assume responsibility for the accuracy and completeness of such statements. The information derived in this report are statements of opinion only, and Cognilytica shall not be held liable in any manner for any conclusions or actions taken pursuant to this report. The information contained herein has been obtained from sources believed to be reliable. Cognilytica shall have no liability for errors, omissions, or inadequacies in the information contained herein or for interpretations thereof. Report purchaser and/or reader assumes sole responsibility for the selection of these materials to achieve its intended results. The opinions expressed herein are subject to change without notice. Cognilytica does not make open its research methods, underlying data, sources, or means and methods of analysis for inquiry, evaluation, or examination.

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