Insights, tips, and product updates

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.

Uncover ML engineers' ultimate guide to picking the top data labeling platform. Master assessing data quality, UI, tools, scalability & security for AI triumphs.

The future has arrived, and it's all thanks to the awe-inspiring power of computer vision technology. Let's dive into the leading-edge applications of computer vision and discover where it can lead us!

Labeling Service Providers are an essential part of the Machine Learning industry. As the market crystalizes in 2023, each player will face heavy challenges to establish their differenciation, remain competitive, and secure business. In this article, we'll dive into a few things LSPs can do to remain leaders in their industry.

Image annotation is long, painful, and inefficient? Let's dive into the value of foundation models, and more specifically the Segment Anything Model (SAM) from Meta, to understand how they can improve the labeling experience.

With Mapper algorithm, a tool from Topological Data Analysis, we create a powerful representation of a dataset in conjunction with a model.

Few shot Named Entity Recognition learning methods have been developed to be able to generalize to new tasks with few labeled examples. We will review several recent methods and see how they are different from traditional learning.

Despite the critical role it plays in the success of labeling projects, building efficient labeling guidelines can be very difficult.  This blog will present the general approach and provide tips based on Kili Technology’s expertise in the construction of such guidelines.

A first experiment to automatically detect labeling errors for image object detection projects in Kili

This tutorial will help you understand that how automated machine learning (AutoML) is used to accelerate your labelling process.