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.
We are proud to announce that CloudFactory has been awarded the ISO 9001:2015 standard in Quality Management System.
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 ...
Realizing AI’s potential benefits - and mitigating its challenges - will require collaboration. As partnerships form and critical questions arise across the globe in government, ...
Given the challenges of hiring and managing a team to complete the arduous data work behind AI, many companies are turning to outside help.
AI innovators rely on external teams to structure data for ML algorithms. But scaling quality data requires the right people & processes in your tech stack.
