Workforce Strategy

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.

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.

Building and maintaining relationships while working remotely calls for creativity. We use pod socials to connect globally at CloudFactory.

Whether you’re a seasoned pro or new to artificial intelligence, continued learning important. Here are a few of our favorite machine learning & AI books.

Some organizations are winning in a pandemic by adapting to changing customer and market demands. We’ve provided adaptive workforces for a decade. Here’s what they look like.

Supervised learning requires a lot of labeled data. Here’s what it takes to design a high-performance data labeling pipeline for machine learning.

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.

Data scientists at Hivemind created 3 data labeling tasks and hired 2 teams to complete them. The differences in data accuracy, speed, and cost may surprise you.

Not all outsourced data labeling partners are a good fit for every AI project. Here are 5 things you need to consider before, during, and after vendor evaluations.

The data annotation. One emerging feature is automation, also known as pre-annotation or auto labeling. This article will focus on some of its benefits and drawbacks.