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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.
This post from the Chromium Blog discusses using machine learning to combat unwanted notification prompts in Chrome.
This post describes Square's development of a RoBERTa-based ML model for accurately categorizing merchants into business types.
This article explains PayPal's Delta CV (Delta Customer Value) framework, which uses causal inference and synthetic control to measure the incremental profit impact of product adoptions and user actions.
This post from the Chromium Blog discusses how machine learning techniques were applied to improve the Chrome address bar (Omnibox) experience on Windows, Mac, and ChromeOS.
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