3D Shape Tokenization

We introduce Shape Tokens, a 3D representation that is continuous, compact, and easy to integrate into machine learning models. Shape Tokens serve as conditioning vectors, representing shape information within a 3D flow-matching model. This flow-matching model is trained to approximate probability density functions corresponding to delta functions concentrated on the surfaces of 3D shapes. By …

Title Launch Observability at Netflix Scale

Part 2: Navigating Ambiguity By: Varun Khaitan With special thanks to my stunning colleagues: Mallika Rao, Esmir Mesic, Hugo Marques Building on the foundation laid in Part 1, where we explored the “what” behind the challenges of title launch observability at Netflix, this post shifts focus to the “how.” How do we ensure every title launches seamlessly …

ML 15583 System Workflow

Efficiently build and tune custom log anomaly detection models with Amazon SageMaker

In this post, we walk you through the process to build an automated mechanism using Amazon SageMaker to process your log data, run training iterations over it to obtain the best-performing anomaly detection model, and register it with the Amazon SageMaker Model Registry for your customers to use it. Log-based anomaly detection involves identifying anomalous …

The PyTorch developer’s guide to JAX fundamentals

Like many PyTorch users, you may have heard great things about JAX — its high performance, the elegance of its functional programming approach, and its powerful, built-in support for parallel computation. However, you may have also struggled to find what you need to get started: a straightforward, easy-to-follow tutorial to help you understand the basics …