Doctor shortages are impacting healthcare innovators’ ability to label data for computer vision solutions. Outsourcing the work will help.
Computer vision improves patient care, streamlines medical decisions, and lowers costs. Ask these questions before outsourcing healthcare data annotation.
Learn 3 key takeaways from our latest LinkedIn Live event where we explored what it takes to combine human and machine intelligence effectively.
From data collection to training and deploying working models, the development of medical AI comes with many important challenges and opportunities.
An incremental design approach to automation and machine learning affords strategic opportunities for choosing to route exceptions to machines or people.
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.
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.
Building and maintaining relationships while working remotely calls for creativity. We use pod socials to connect globally at CloudFactory.
Even in uncertain times, you’re swimming in an ocean of data. If you’re using AI, how you process and use that data will determine the future of your business.