CloudFactory Blog

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. How you are processing data that powers AI 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.

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

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 ...

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.

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.

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.