Avoid the Challenge of Achieving High-Quality Data

Avoid the Challenge of Achieving High-Quality Data

Data quality is clearly paramount for a successful AI project, but how difficult is it to achieve?

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How to Take the Security Risk Out of Outsourcing Your Data Labeling

How to Take the Security Risk Out of Outsourcing Your Data Labeling

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 ...

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5 Strategic Steps for Choosing Your Data Labeling Tool

5 Strategic Steps for Choosing Your Data Labeling Tool

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

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The 3 Hidden Costs of Crowdsourcing for Data Labeling

The 3 Hidden Costs of Crowdsourcing for Data Labeling

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.

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Scaling Quality Training Data: Choosing the People in Your AI Tech Stack

Scaling Quality Training Data: Choosing the People in Your AI Tech Stack

AI innovators rely on external teams to structure data for ML algorithms. But scaling quality data requires the right people & processes in your tech stack.

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