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Dropbox Dash built a custom hybrid feature store to power real-time AI ranking across tens of thousands of work documents.
Pinterest Search presents a methodology for scaling search relevance assessment using fine-tuned LLMs to replace costly human annotation.
Pinterest describes how Pinner (user) surveys are used to train a machine learning model that improves content quality recommendations across Homefeed, Related Pins, and Search.
Pinterest shares its strategic shift toward fine-tuned open-source AI models, achieving comparable performance at less than 10% the cost of proprietary models.
Amazon Bedrock introduces reinforcement fine-tuning, enabling developers to build accurate AI models via feedback without ML expertise.
Amazon SageMaker AI introduces serverless customization to streamline fine-tuning of popular AI models including Amazon Nova, DeepSeek, Llama, and Qwen.
Amazon SageMaker HyperPod introduces two new AI model training features: checkpointless training and elastic training.
This post describes how Lyft evolved LyftLearn, their end-to-end ML platform, from a fully Kubernetes-based system to a hybrid architecture combining AWS SageMaker and Kubernetes.
Grab built a custom ~1B Vision LLM to improve eKYC document processing for Southeast Asian languages and documents.
This post introduces Half-Quadratic Quantization (HQQ), a calibration-free quantization method for large machine learning models that achieves calibration-based quality at data-free speeds.
This post describes a Lyft data scientist's starter project using the Rider Experience Score (RES) tool to estimate long-term causal effects of rider experiences on retention without relying on A/B tests.
This post outlines Dropbox's systematic evaluation framework for conversational AI, developed while building Dropbox Dash.
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