Data Collection
Data collection involves gathering and preparing information from various sources for analysis. We provide reviews and comparisons of data collection tools, explore common challenges, and cover approaches like AI data collection and data crowdsourcing.
AI Data Collection: Risks, Challenges & Tools
AI builders need fresh, high quality data: However, data collection comes with its risks. For example, enterprises need to avoid unethical data collection practices and ensure that data is collected ethically to minimize reputational risk.
Top 6 AI Data Collection Challenges & Solutions
AI adoption was slightly lower last year (Figure 1); one reason could be the various challenges in implementing AI. Training data collection has been identified as one of the main barriers to AI adoption. To avoid data-related challenges, businesses are opting to work with AI data collection services.
Top 4 Facial Recognition Data Collection Methods
Despite the controversies surrounding this technology, the facial recognition systems (FRS) market continues to grow. Facial recognition applications are everywhere, from helping improve mental disorder diagnoses to finding fugitives. Developing and improving these systems requires facial data, which sometimes can be challenging to obtain due to security and privacy-related concerns of people.
Video Data Collection: Challenges & Best Practices
Video data is crucial for training computer vision (CV) systems, particularly with the increasing demand for autonomous vehicles and CV-enabled technologies. Here, we explore what video data collection entails, the challenges involved, and best practices to consider.
Top 3 Amazon Mechanical Turk Alternatives
This analysis explores some downsides to using Amazon Mechanical Turk, or MTurk, a popular AI data collection and market survey platform. It also compares the top Amazon Mechanical Turk alternatives on the market. Readers interested in MTurk alternatives usually fall under 3 categories; select yours to see relevant alternatives for your business.
Telus International Review & Alternatives
As AI-powered solutions become increasingly abundant, many businesses rely on AI data services to acquire the necessary data to fuel their AI projects, such as large language models (LLMs) or natural language processing (NLP) systems. Telus International is one such service that helps businesses with their AI data needs.
Dataforce by TransPerfect Review & Alternatives
Our readers interested in Dataforce usually fall under the following two categories; click on yours to see relevant information: See an in-depth review of Dataforce by TransPerfect, which compares Dataforce with its top 3 alternatives. Top 3 Dataforce alternatives (for contributors) Table 1.
Top 6 Data Collection Methods for AI and Machine Learning
While some companies rely on AI data collection services, others gather their data using scraping tools or other methods. See the top 6 AI data collection methods and techniques to fuel your AI projects with accurate data: Overview of AI data collection methods 1. Crowdsourcing Online talent platforms, such as crowdsourcing platforms, have various benefits.
Data Collection: 10+ Methods Across 6 Key Use Cases
The role of data has become paramount for digitally transforming enterprises. Whether it’s marketing or AI data collection, businesses have become increasingly reliant on accurate data collection to make informed decisions; it’s essential to have a clear strategy in place. This article explores the top techniques for data collection across different sectors and use cases.
5 Steps to OCR Training Data
The interest in optical character recognition (OCR) and intelligent character recognition (ICR) technology is falling as companies switch to more automated solutions, such as machine learning-enabled data extraction. However, due to its various benefits, many companies still use1 or plan to use tools powered by OCR technology in their paper-based operations.