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From faster drug discovery to robotic surgery, AI is transforming the healthcare sector with promises of improved patient care and reduced costs.
CloudFactory Blog
An incremental design approach to automation and machine learning affords strategic opportunities for choosing to route exceptions to machines or people.
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.
Automation and AI hold great potential to innovate, improve, and make predictions. Here are three mistakes you’ll want to avoid.
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.
Could the secret to developing ML be more boring than we think? It’s time to give up the quest for the perfect model.
What will 2021 bring to the world of AI and machine learning? CloudFactory CEO and founder Mark Sears shares our predictions.
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.
People are involved in everything from training and testing algorithms to labeling data, conducting quality control, and monitoring automation.
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.
