Data Management

Data-Centric AI: A Guide to Improving ML Performance Through Data

How to Deal with Imbalanced Datasets in Computer Vision

Imbalanced datasets lead to problems with accuracy, overfitting, and bias. Data augmentation, class weighting and hierarchical classification can help.

How To Ensure Data Quality – Best Practices

Data curation is the management of data in an organization such that it is readily available in the present and preserved for future use

How to Reduce Training Data with Self-Supervised Learning

Self-Supervised Learning (SSL) aims to leverage large unlabeled datasets to train capable feature extractors such as CNN or ViT encoders. But what is SSL?

What Is Image Data Augmentation?

To solve the problem of data scarcity, we use data augmentation techniques. But how do you augment image data? We'll go throw this in a simple way.

Data Versioning & Feedback Loop Best Practices

Data versioning is an effective methodology used when running many experiments that entail different data processing techniques. Find our best practices!

Data Management in AI: Key Success Factor

Research reveals that 50% if AI teams is spent in developing AI on training data, and 15% augmenting datasets to optimize processes around training data.

How to Ensure Image Dataset Quality In Image Classification

Learn how to tackle the dataset quality main issues, and how to optimize the overall quality of your AI models for image classification use-cases.

4 Things to Consider To Choose a Data Management Platform

Working with large datasets can be challenging. If you don't know what elements to consider when picking a data AI platform, then this article is for you.