CloudFactory Blog

Quality data is the lifeblood of great computer vision applications. Here’s a closer look at best practices for creating your own custom data sets.

Great computer vision applications require quality visual data, and a lot of it. Here are your options when you need data.

Data preparation is the most time-consuming part of machine learning. Here are a few tips for getting it right.

Agriculture data is complex. Annotating agtech data often requires help from agronomists. We help Hummingbird Tech overcome that AI product development hurdle.

AI is transforming healthcare by arming practitioners with more information at the right times to make better decisions and fewer errors. Here’s how image annotation makes it ...

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.

Images and videos are both means to an end to annotate visual data. Each may have its own unique process but in the end individual frames are being annotated on a meta data level. ...

Image annotation holds great promise for these centuries-old industries that rely heavily on visual data and are ripe for AI innovation.

CloudFactory and V7 Labs annotated chest x-rays and trained ML models to identify issues including COVID-19. The dataset is now available for research.

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.

Some data annotation tools won't be a good fit for your AI and machine learning project. Keep these six important features in mind as you evaluate tool providers.

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.

Your in-house data scientists shouldn't be doing tedious data labeling work for machine learning projects. They should be focusing on more important innovation.

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.