Recipe
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Data Labeling for Machine Learning

Collaborators
Reviewers
Total time needed: ~55 minutes
Learning Objectives
Learn about types of data labeling, tools for labeling, maintaining data quality, security, and scaling
Potential Use Cases
Important factors to consider before data annotation and labeling tool selection
Target Audience
BEGINNERML Engineers, Data Engineers, Data Scientists wanting to label data
Go through the following annotated items in order:
ARTICLE 1. Introduction to Data Labeling
  • What is data labeling ?
  • How does data labeling work ?
7 minutes
ARTICLE 2. Types of Data Labeling (Annotation)
  • How to label different data like text, images, video or audio?
  • Do you need to handle each datatype in a specific way?
10 minutes
ARTICLE 3. Ensuring Quality and Accuracy of Data Labels
  • How do you ensure high quality data labels over a large dataset?
  • Is inhouse labeling more accurate than crowdsourced labeling?
10 minutes
ARTICLE 4. Scaling the Data Labeling Process
  • How to create a scalable and sustainable data labeling solution?
10 minutes
ARTICLE 5. Security during Data Labeling
  • What are risks when outsourcing data labeling?
  • How do you maintain security for data labeling jobs?
10 minutes
ARTICLE 6. Common Tools for Data Labeling
  • What are the common data labeling tools available online?
8 minutes

Concepts Covered