CloudFactory Blog

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.

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.

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

It is not enough for workforce vendors to tout a history of quality and speed. They must prove they can serve your needs during and after COVID-19.

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 ...

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