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How to filter redundant data

Many interesting Deep learning applications rely on the use of complex architectures fueled by large datasets. However, when doing so, a new challenge surfaces: data redundancy.

NVIDIA Blackwell B200 vs H100: Real-World Benchmarks, Costs, and Why We Self-Host

The B200 is up to 57% faster for model training than the H100, up to 10x cheaper to run when self-hosted, and we’ve broken down all the costs, performance metrics, and power consumption data inside.

Efficient Training for Multimodal Vision Models: Techniques and Trade-offs

This article explores the evolution and key design choices in training multimodal vision language models (VLMs). It examines two main architectural approaches: cross-attention (pioneered by Flamingo) and self-attention (used in FROMAGe and BLIP2). We highlight how most modern VLMs build upon pre-trained unimodal backbones rather than training from scratch and discuss various techniques to boost performance, including masked training and resolution adaptation. It also outlines the typical three-stage training process: pre-training, supervised fine-tuning, and alignment, each serving distinct purposes in model development.

Top Computer Vision Tools for ML Engineers in 2025

Discover the leading computer vision tools of 2025 in data labeling, curation, model development, deployment, and MLOps. An in-depth, technical review for ML engineers seeking the best open-source and enterprise solutions.

The Engineer's Guide to Self-Supervised Learning

Learn what self-supervised learning is and how engineers can use it to train AI models with minimal labeled data. This guide explores key techniques, real-world applications, and the benefits of self-supervised learning in computer vision and machine learning.