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.

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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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.

Deep learning platform MissingLink and the MIT Media Lab brought together AI innovators in Boston last month for a meetup about how AI is changing the world for the better.

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.

Realizing AI’s potential benefits - and mitigating its challenges - will require collaboration. As partnerships form and critical questions arise across the globe in government, ...