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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 …