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How Rufus doubled their inference speed and handled Prime Day traffic with AWS AI chips and parallel decoding Shruti Dubey AWS Machine Learning Blog

​[[{“value”:” Large language models (LLMs) have revolutionized the way we interact with technology, but their widespread adoption has been blocked by high inference latency, limited throughput, and high costs associated with text generation. These inefficiencies are particularly pronounced during high-demand events like Amazon Prime Day,… Read More »How Rufus doubled their inference speed and handled Prime Day traffic with AWS AI chips and parallel decoding Shruti Dubey AWS Machine Learning Blog

Interleaved Reasoning for Large Language Models via Reinforcement Learning Apple Machine Learning Research

​Long chain-of-thought (CoT) significantly enhances large language models’ (LLM) reasoning capabilities. However, the extensive reasoning traces lead to inefficiencies and an increased time-to-first-token (TTFT). We propose a novel training paradigm that uses reinforcement learning (RL) to guide reasoning LLMs to interleave thinking and answering for… Read More »Interleaved Reasoning for Large Language Models via Reinforcement Learning Apple Machine Learning Research

Foundation Model Hidden Representations for Heart Rate Estimation from Auscultation Apple Machine Learning Research

​[[{“value”:”Auscultation, particularly heart sound, is a non-invasive technique that provides essential vital sign information. Recently, self-supervised acoustic representation founda- tion models (FMs) have been proposed to offer insights into acoustics-based vital signs. However, there has been little exploration of the extent to which auscultation is… Read More »Foundation Model Hidden Representations for Heart Rate Estimation from Auscultation Apple Machine Learning Research

New Amazon Bedrock Data Automation capabilities streamline video and audio analysis Ashish Lal AWS Machine Learning Blog

​[[{“value”:” Organizations across a wide range of industries are struggling to process massive amounts of unstructured video and audio content to support their core business applications and organizational priorities. Amazon Bedrock Data Automation helps them meet this challenge by streamlining application development and automating workflows… Read More »New Amazon Bedrock Data Automation capabilities streamline video and audio analysis Ashish Lal AWS Machine Learning Blog

Selecting the Right Feature Engineering Strategy: A Decision Tree Approach Iván Palomares Carrascosa MachineLearningMastery.com

​In machine learning model development, feature engineering plays a crucial role since real-world data often comes with noise, missing values, skewed distributions, and even inconsistent formats. In machine learning model development, feature engineering plays a crucial role since real-world data often comes with noise, missing values,… Read More »Selecting the Right Feature Engineering Strategy: A Decision Tree Approach Iván Palomares Carrascosa MachineLearningMastery.com

GuardianGamer scales family-safe cloud gaming with AWS Heidi Vogel Brockmann, Ronald Brockmann AWS Machine Learning Blog

​[[{“value”:” This blog post is co-written with Heidi Vogel Brockmann and Ronald Brockmann from GuardianGamer. Millions of families face a common challenge: how to keep children safe in online gaming without sacrificing the joy and social connection these games provide. In this post, we share… Read More »GuardianGamer scales family-safe cloud gaming with AWS Heidi Vogel Brockmann, Ronald Brockmann AWS Machine Learning Blog

CLIP-UP: A Simple and Efficient Mixture-of-Experts CLIP Training Recipe with Sparse Upcycling Apple Machine Learning Research

​Mixture-of-Experts (MoE) models are crucial for scaling model capacity while controlling inference costs. While integrating MoE into multimodal models like CLIP improves performance, training these models is notoriously challenging and expensive. We propose CLIP-Upcycling (CLIP-UP), an efficient alternative training strategy that converts a pre-trained dense… Read More »CLIP-UP: A Simple and Efficient Mixture-of-Experts CLIP Training Recipe with Sparse Upcycling Apple Machine Learning Research