Insights, tips, and product updates

Data Augmentation, Advanced Labeling techniques: Direct, Semi-supervised, Active Learning, Weak Supervision.

Every CV application relies on high-quality data, which necessitates a strong image annotation process. This enables data scientists and computer vision experts to train models more effectively. However, maintaining data quality is challenging due to growing data volumes and complexity. Check out our guide for best practices in creating top-notch datasets for your models.

Unleash the Power of Falcon-40B: Enhancing Data Labeling Efficiency and Accuracy in Machine Learning Development. Explore how this remarkable 40-billion-parameter LLM revolutionizes downstream tasks.

Explore 6 open source large language models and discover how to evaluate and fine-tune them for your data labeling needs.

While the digital world is moving from a code-centric to a data-centric paradigm, building datasets remains a complex task. Many companies around the world are currently trying to make dataset building as easy and efficient as possible. Among them, two contestants stand out: Kili Technology and Quality Match. Let's dive into how to use them to your advantage!

Video Object Detection is a Computer Vision task, meant to analyze video images and classify objects located in them.

Scale up efficiency of your manufacturing processes using AI with defect detection, waste management and customer order processing.

High-quality, accurately-labeled data is crucial to your success, but how to select the labeling tool that will drive your project to success? Here are our answers.

Introduction on 5 best video labelling practices to make your AI project succeed.

Machine learning can handle structured data and unstructured data. Yet, the main steps remain the same: you collect training data, you train a model, then you can evaluate it and eventually deploy it in production. However, depending on if you process structured or unstructured data, the methods you will need to use may look really different. What is the difference between structured and unstructured data? And how does it impact the building of your Machine Learning pipeline?

Unlock the power of your AI projects with data labeling tools. Learn how these tools enhance data quality, reduce costs, and bolster AI performance. Discover what features to look for in the best tools and the value they can bring to your AI development process.

Welcome to the exciting world of Synthetic Data in Machine Learning! With its ability to overcome data scarcity, preserve privacy, and reduce costs, synthetic data is a game-changer in the field of AI. In this guide, you'll learn how to generate it, and explore its numerous applications in healthcare, robotics, finance, and beyond. Ready? Read on.

Data labeling is an essential part of many machine-learning project. As the quality of your data labeling will impact your ML model performances, deciding whether to buy or build your own data labeling solution can be difficult. In this article, we'll discuss the pros and cons of both options to help you decide which is best for your project.

Find the best image segmentation tool out there. From partitioning an image into multiple segments to labelling those in the desired manner.

Discover the Evolution of Image Classification in Computer Vision. Learn about Techniques, Use Cases & Challenges. Explore Deep Learning Models & Future Trends

Speed up chatbot dataset training using counterfactual data augmentation for better efficiency.

Using smart tools will help you lower the costs of pixel perfect segmentation and, therefore, train your models with the best data. Read on to discover how Superpixels will improve your data annotation projects.

Get to know some common annotation errors and how you can avoid them, image annotation errors-Kili Technology

In this tutorial, we will show how to work with Kili and YOLO v7 to produce a SOTA-grade object detection system.

AI-powered apps fuel more and more companies' growth, and regularly gets involved in GAFAM-related controversies, reliable AI gets more and more coverage. Still, many stakeholders are reluctant to invest in its building. Here's why you should, and how to do so.