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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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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Will AI Replace the Humans In the Loop?

Will AI Replace the Humans In the Loop?

People are involved in everything from training and testing algorithms to labeling data, conducting quality control, and monitoring automation.

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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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Driven by AI: Driver Technologies Helps Protect the World’s Motorists

Driven by AI: Driver Technologies Helps Protect the World’s Motorists

Autonomous vehicles and AI driver safety tools aren’t affordable for all. Driver Technologies made a free innovative app and model training database.

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Under the Hood: Active Learning and Autonomous Vehicles

Under the Hood: Active Learning and Autonomous Vehicles

Autonomous vehicles require continuous training to ensure they operate safely. Here’s how active learning can improve that process.

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Autonomous Car Machine Learning and Data Collection

Autonomous Car Machine Learning and Data Collection

Discover the challenges of autonomous vehicle data collection and machine learning training. Learn how CloudFactory supports AV innovators with quality data annotation.

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Speed Bumps Ahead: Training Data Hurdles for Autonomous Vehicles

Speed Bumps Ahead: Training Data Hurdles for Autonomous Vehicles

From localized bias to difficulties annotating video and radar data, here are some of the biggest challenges facing the future of autonomous vehicles.

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AI in Action: The Rise of Autonomous Vehicles

AI in Action: The Rise of Autonomous Vehicles

In the Rise of Autonomous Vehicles the world of transportation and logistics is rapidly changing. Here are 3 AV innovators you should know about.

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How Computer Vision Helps Us See the Future

How Computer Vision Helps Us See the Future

Can AI help us in predicting the future with computer vision? Here's how computer vision can use today’s data to model tomorrow’s outcomes.

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4 Common Misconceptions About Image Annotation for Computer Vision

4 Common Misconceptions About Image Annotation for Computer Vision

Image annotation is an important task when training a computer vision model. Here are common misconceptions about image annotation for computer vision.

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5 Reasons You Should Work at CloudFactory

5 Reasons You Should Work at CloudFactory

CloudFactory is a great place to be because our teams work on some of the world’s most innovative technologies and because we’re on a mission: to create work for one million ...

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6 Steps to Create Custom Data Sets for Computer Vision

6 Steps to Create Custom Data Sets for Computer Vision

Quality data is the lifeblood of great computer vision applications. Here are 6 best practices for creating your own custom data sets for computer vision.

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3 Ways to Acquire Data Sets for Computer Vision

3 Ways to Acquire Data Sets for Computer Vision

Great computer vision applications require a lot of quality visual data. Here's how you can acquire quality data sets for computer vision applications.

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