How to knockdown image segmentation roadblocks that derail AI

How to knockdown image segmentation roadblocks that derail AI

This blog covers image segmentation challenges in data labeling & how quality data improves model accuracy and reduces time to market for AI projects.

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Image segmentation best practices: An infographic for business leaders

Image segmentation best practices: An infographic for business leaders

Learn image segmentation best practices for smarter decisions, boosted revenue, and streamlined operations. Our infographic covers the challenges and solutions.

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7 interesting LiDAR applications

7 interesting LiDAR applications

LiDAR is a useful 3-D object detection technology for many industries, from AV to aerial inspections. Here are 7 interesting applications of LiDAR.

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AI-powered labeling: the key to profitable AI development in agtech

AI-powered labeling: the key to profitable AI development in agtech

How CTOs and VPs of product and machine learning can navigate key agtech hurdles using AI-powered data labeling for sustainable growth and profitability.

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The importance of humans in the loop in AI development

The importance of humans in the loop in AI development

While foundational models offer remarkable potential, our experience reveals that humans in the loop remain crucial for successful AI development.

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Active learning: tackling ML data annotation challenges

Active learning: tackling ML data annotation challenges

This post discusses accelerating AI development with active learning, a machine learning method that prioritizes the most informative data for labeling.

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Supercharge Vision AI projects with foundation models

Supercharge Vision AI projects with foundation models

Discover the power of foundation models for Vision AI. Supercharge your workflow, save time, and boost performance.

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Creating data annotation guidelines that drive accurate ML models

Creating data annotation guidelines that drive accurate ML models

A resource to create A+ data annotation guidelines that will lead to high-quality AI models, cost savings, and improved business operations.

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The 4 layers of automated data labeling for faster AI goals

The 4 layers of automated data labeling for faster AI goals

Uncover the four layers of automated data labeling—pre-labeling, pre-trained models, hosting, and technology—to achieve faster and smarter AI goals.

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Building a machine learning solution foundation: For business leaders

Building a machine learning solution foundation: For business leaders

This blog post uses Maslow's hierarchy of needs to illustrate how business leaders can build a machine learning (ML) solution foundation.

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Building a machine learning solution foundation: For technical leaders

Building a machine learning solution foundation: For technical leaders

ML models need a foundation of data, annotation, and optimization to succeed. Learn how with a technical spin on Maslow's hierarchy of needs.

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Top AI innovators who value high-quality data labeling

Top AI innovators who value high-quality data labeling

AI innovators who value high-quality data labeling. We help scale data annotation with skilled annotators, predictable pricing, and quick turnaround times.

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Generative AI takes center stage at The AI Summit

Generative AI takes center stage at The AI Summit

The AI Summit uncovered the potential of generative AI, with a spotlight on ethical AI, benefits to creatives, and the critical role of humans in the loop.

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Accelerated Annotation adds keypoint and video understanding to roster

Accelerated Annotation adds keypoint and video understanding to roster

CloudFactory introduces keypoint and video understanding to Accelerated Annotation data annotation tool.

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Building a ChristmasGAN (in July!)

Building a ChristmasGAN (in July!)

Experience "Christmas in July" with a ChristmasGAN project. Explore Generative Adversarial Networks, image translation, and creative Christmas touches.

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Human in the loop: Will it always be a big deal?

Human in the loop: Will it always be a big deal?

Even as companies shift from a model-centric to a data-centric AI focus, humans in the loop remain critical in accelerating AI development.

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