CloudFactory Blog

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.

Many companies are having to contend with new data security concerns associated with their employees accessing important data from home.

CloudFactory responds to the COVID-19 pandemic with remote access for workers, business continuity for clients, and community service.

No matter how robust your initial training may be, keeping your machine learning models up-to-date is essential. Here are two retraining approaches.

It takes a lot of time and resources to prepare and label data. Learn why outsourcing the data preparation to a managed workforce partner is a good business decision.

The level of data quality you'll receive from data labeling providers depends on several workforce, QA and tooling factors. Here are 6 ways some data labeling providers put your ...

The people, processes, and tools used by outsourced data labeling partners make a big difference in final data quality. Here are 3 signs that you'll receive quality work from your ...

CloudFactory will never ask for sensitive personal information or payment as part of the employment process.

Achieving a high level of accuracy in data labeling is vital. This concept can be understood if we think about a mural of Rubik’s Cubes®.

Any problem (like a Rubik’s cube®) is solvable with a documented process.

How solving a Rubik’s cube® is like labeling your unstructured data.

Mark Sears, founder and CEO, shares how CloudFactory will use its latest round of funding, a Series-C round led by FTV Capital.

Melody Ayeli, who reviews AI projects for Toyota’s CIO, shared insights on common AI failure points in a session at AI Summit in San Francisco.

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 ...