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How Amazon trains sequential ensemble models at scale with Amazon SageMaker Pipelines

Amazon SageMaker Pipelines includes features that allow you to streamline and automate machine learning (ML) workflows. This allows scientists and model developers to focus on model development and rapid experimentation rather than infrastructure management Pipelines offers the ability to orchestrate complex ML workflows with a simple Python SDK with the ability to visualize those workflows …

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Orchestrating GPU-based distributed training workloads on AI Hypercomputer

When it comes to AI, large language models (LLMs) and machine learning (ML) are taking entire industries to the next level. But with larger models and datasets, developers need distributed environments that span multiple AI accelerators (e.g. GPUs and TPUs) across multiple compute hosts to train their models efficiently. This can lead to orchestration, resource …

BayesCNS: A Unified Bayesian Approach to Address Cold Start and Non-Stationarity in Search Systems at Scale

Information Retrieval (IR) systems used in search and recommendation platforms frequently employ Learning-to-Rank (LTR) models to rank items in response to user queries. These models heavily rely on features derived from user interactions, such as clicks and engagement data. This dependence introduces cold start issues for items lacking user engagement and poses challenges in adapting …

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Accelerate your ML lifecycle using the new and improved Amazon SageMaker Python SDK – Part 2: ModelBuilder

In Part 1 of this series, we introduced the newly launched ModelTrainer class on the Amazon SageMaker Python SDK and its benefits, and showed you how to fine-tune a Meta Llama 3.1 8B model on a custom dataset. In this post, we look at the enhancements to the ModelBuilder class, which lets you seamlessly deploy …

How Dun & Bradstreet is transforming software development with Gemini Code Assist

Dun & Bradstreet, a leading global provider of business data and analytics, is committed to maintaining its position at the forefront of innovation. For the past two years, this commitment has included the company’s deliberate approach to improving its software development lifecycle by infusing AI solutions.  While development velocity and security were the company’s most …

Evaluating Gender Bias Transfer between Pre-trained and Prompt-Adapted Language Models

*Equal Contributors Large language models (LLMs) are increasingly being adapted to achieve task-specificity for deployment in real-world decision systems. Several previous works have investigated the bias transfer hypothesis (BTH) by studying the effect of the fine-tuning adaptation strategy on model fairness to find that fairness in pre-trained masked language models have limited effect on the …

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How AWS sales uses Amazon Q Business for customer engagement

Earlier this year, we published the first in a series of posts about how AWS is transforming our seller and customer journeys using generative AI. In addition to planning considerations when building an AI application from the ground up, it focused on our Account Summaries use case, which allows account teams to quickly understand the …

Unwrap 12 days of training to learn generative AI this December

Tis the season for learning new skills! Get ready for 12 Days of Learning, a festive digital advent calendar packed with courses, hands-on labs, videos, and community opportunities—all designed to boost your generative AI expertise. Discover a new learning resource on Google Cloud’s social channels every day for twelve days this December.  Before you start: …

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How Palantir Integrates Threat Modeling Into Product Development (Software Supply Chain Series, #2)

Editor’s Note: This is the second post in a series that shares insights from our journey to enhance our software supply chain security story at Palantir. This post provides background on how Palantir integrates threat modeling into product development. Introduction Palantir has always placed security as a first-order consideration in our products and it serves …