Natural language processing (NLP) is a fast-growing AI technology, but data labeling for NLP is complex. Here is what to look for in an NLP data workforce.
Learn about SOC 2 certification and how it impacts CloudFactory’s data security policies, procedures and tools for our remote data labeling workforce.
Learn how ISO/IEC 27001:2013 certification reinforces the confidence CloudFactory clients already have in our data and information security practices.
Learn how CloudFactory’s ISO 9001:2015 certification in Quality Management Systems validates our workforce, processes, and commitment to data quality.
NLP is one of the most difficult AI applications to develop and maintain. When you outsource data labeling, make sure you choose the right team.
Optical character recognition (OCR) can improve productivity when transcribing text, but people still play a critical role in quality control.
Choosing a managed workforce to take care of your data entry needs can help overcome the challenges of scale, quality control, and communication.
Accurate data entry is the foundation of strong financial practices. It helps finance teams maintain complete records and expedite critical transactions.
Data entry is an essential part of the digital transformation process for legal firms seeking to speed discovery and provide better client experiences.
Data entry is a crucial part of any digital transformation project. Sometimes it makes more sense to outsource than to burden your own team.
How can you determine if a data labeling service will deliver quality work? How they communicate and handle quality control are key indicators.
How can you determine if a data labeling service will deliver quality work? It starts with their vetting, hiring, and training processes.
People have unconscious biases that affect hiring decisions. People also can hard-code their biases into an AI system. Humans in the loop can help.
Humans play a critical role throughout the AI lifecycle, from data cleaning and labeling to quality control and automation monitoring.
Developing ML models requires a lot of data and skilled people to work with it. Here’s our HITL approach for machine learning model development.
When you have massive data to label for machine learning, it makes sense to outsource it. But what happens when your data is sensitive, protected, or private? Here’s a quick ...