Should You Outsource Data Labeling for NLP?

Should You Outsource Data Labeling for NLP?

NLP is one of the most difficult AI applications to develop and maintain. When you outsource data labeling, make sure you choose the right team.

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OCR and People: Your Dynamic Data-Entry Duo

OCR and People: Your Dynamic Data-Entry Duo

Optical character recognition (OCR) can improve productivity when transcribing text, but people still play a critical role in quality control.

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3 Signs You Need A Managed Workforce for Data Entry

3 Signs You Need A Managed Workforce for Data Entry

Choosing a managed workforce to take care of your data entry needs can help overcome the challenges of scale, quality control, and communication.

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3 Ways Finance Benefits from Data Entry Services

3 Ways Finance Benefits from Data Entry Services

Accurate data entry is the foundation of strong financial practices. It helps finance teams maintain complete records and expedite critical transactions.

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Data Entry Services Fuel Transformation in Legal Industry

Data Entry Services Fuel Transformation in Legal Industry

Data entry is an essential part of the digital transformation process for legal firms seeking to speed discovery and provide better client experiences.

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4 Reasons You Should Outsource Data Entry

4 Reasons You Should Outsource Data Entry

Data entry is a crucial part of any digital transformation project. Sometimes it makes more sense to outsource than to burden your own team.

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Choosing a Data Labeling Service Part 2: Communication & Quality Control

Choosing a Data Labeling Service Part 2: Communication & Quality Control

How can you determine if a data labeling service will deliver quality work? How they communicate and handle quality control are key indicators.

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CloudFactory Awarded ISO 9001:15 standard in quality management.

CloudFactory Awarded ISO 9001:15 standard in quality management.

We are proud to announce that CloudFactory has been awarded the ISO 9001:2015 standard in Quality Management System.

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Choosing a Data Labeling Service Part 1: Hiring and Vetting

Choosing a Data Labeling Service Part 1: Hiring and Vetting

How can you determine if a data labeling service will deliver quality work? It starts with their vetting, hiring, and training processes.

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What Can We Learn From HR About AI Bias?

What Can We Learn From HR About AI Bias?

People have unconscious biases that affect hiring decisions. People also can hard-code their biases into an AI system. Humans in the loop can help.

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3 Ways Humans in the Loop Add Value to the AI Lifecycle

3 Ways Humans in the Loop Add Value to the AI Lifecycle

Humans play a critical role throughout the AI lifecycle, from data cleaning and labeling to quality control and automation monitoring.

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Accelerating ML Model Development with Human in the Loop

Accelerating ML Model Development with Human in the Loop

Developing ML models requires a lot of data and skilled people to work with it. Here’s our HITL approach for machine learning model development.

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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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Global Partnerships Reflect AI’s Immense Power and Potential Risks

Global Partnerships Reflect AI’s Immense Power and Potential Risks

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

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Successful AI Development Means Fielding The Best Team

Successful AI Development Means Fielding The Best Team

Given the challenges of hiring and managing a team to complete the arduous data work behind AI, many companies are turning to outside help.

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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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