CloudFactory Blog

Azavea's mission is to create advanced geospatial technology and research for civic and social impact. They interviewed a handful of leading data labeling firms, and studiously ...

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 ...

Your choices about tooling and workforce will be important factors in your success as you design, test, validate, and deploy any ML model.

Crowdsourcing seems to offer a cheap option for training machine learning models, but it’s rarely as inexpensive as it seems. Here are some of the hidden costs of the crowd.

Cloud workforce solutions and integrations with leading data labeling platforms support higher quality training data for machine learning.

AI is only as good as the data it's trained to analyze. CloudFactory CEO Mark Sears shares in Forbes about how AI bias can arise from people, tools, algorithms, and human ...

What is a data scientist and what do they do? Explore their roles, responsibilities, and contributions to AI and machine learning.

Learn how the operations team for a popular cash-back retail rewards app scaled speed and accuracy for a data-verification process during the busiest season of the year.

We look at the leading causes of dirty data, which research shows is the most common problem for people who work with data.

The combination of big data and machine learning can unlock the value of data you already have to gain a competitive edge for your business.

Humans are the key to developing the datasets and algorithms required to train intelligent virtual assistants so they can mimic human intelligence.

The future of work has shifted in the last decade. Here are a few ways to stay competitive as our world becomes increasingly driven by artificial intelligence.

CloudFactory Raises $7.3M Series B to Accelerate WorkStreams. The funding will be used to fuel adoption of their alternative to outsourcing and crowdsourcing.

Automation needs humans to address complex tasks that computers simply aren’t equipped to handle.

We help our customers prepare the datasets for their computer and machine vision algorithms.

Humans are the builders and the trainers of every AI technology, and humans will be the cornerstone of work for a long time to come.