AI, LLM Training, and Operations Efficiency

Behind the data: Shahira Ellaboudy

Welcome to our "Behind the Data" series, where we delve into engaging discussions with our top data annotators. This series shines a spotlight on the individuals who play a pivotal role in the development and training of AI.

Beginners Guide to Precision and Recall in Machine Learning

This article provides an in-depth look at precision and recall, two critical metrics in machine learning. It explains their importance, how to calculate them, and when to prioritize one over the other. We've also looked at practical examples and discussed the trade-offs involved in balancing these metrics, particularly in imbalanced datasets.

Federated Learning in Computer Vision Explained

This article discusses how federated learning changes computer vision by training AI models without sharing raw data. It solves privacy issues and improves model accuracy, using examples like smartphones that are getting better at predicting text. We cover how federated learning works, its challenges, and how to solve them. Finally, we look at real-world uses in medical imaging, smart surveillance, self-driving cars, retail, farming, and smart home device

Optical Character Recognition (OCR) Meaning, How it Works and Use Cases

This article explores the transformative impact of Optical Character Recognition (OCR) technology across various industries. It highlights how OCR converts text from scanned documents and images into computer-readable data, enhancing efficiency and accuracy in the banking, healthcare, tourism, and communication sectors. We'll also examine the step-by-step guide on how OCR works and check out the benefits and challenges of implementing OCR technology.

Beginners Guide to One-shot Learning

In the article, we discuss one-short learning, a computer vision model that uses only one example per data category instead of many to teach machine models. We go deeper to compare its counterparts and also check out its use cases.

The Complete Guide to Few-Shot Learning

Few-shot learning is a machine learning model that works with few labeled examples. The article describes how few-shot learning is used in various fields, such as natural language processing, computer vision, healthcare, and speech recognition. We outline different approaches, including meta-learning, data-level methods, parameter-level methods, generative techniques, and more that you need to check.

Understanding Model Drift In Machine Learning

In this guide, we'll explore different types of model drift, including concept and data drift, and discuss how to detect and tackle these issues. We'll also share some practical strategies for continuous retraining, model versioning, and monitoring performance metrics to keep your machine-learning models effective over time.

Exploring Object Detection Techniques Using the COCO Dataset

The article explores object detection techniques using the COCO dataset, a prominent resource in computer vision. It covers the basics of the COCO dataset, its detailed annotations, and how it supports various computer vision tasks such as semantic segmentation, instance segmentation, panoptic segmentation, keypoint detection, and dense pose estimation. The article also compares the COCO dataset with the Open Images Dataset (OID), highlighting their strengths and suitable applications to help researchers and developers choose the right dataset for their projects.