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Your guide to generative AI and ML at AWS re:Invent 2024

AWS Machine Learning Blog

The excitement is building for the fourteenth edition of AWS re:Invent, and as always, Las Vegas is set to host this spectacular event. The sessions showcase how Amazon Q can help you streamline coding, testing, and troubleshooting, as well as enable you to make the most of your data to optimize business operations.

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Getir end-to-end workforce management: Amazon Forecast and AWS Step Functions

AWS Machine Learning Blog

Getir was founded in 2015 and operates in Turkey, the UK, the Netherlands, Germany, and the United States. In this post, we describe the end-to-end workforce management system that begins with location-specific demand forecast, followed by courier workforce planning and shift assignment using Amazon Forecast and AWS Step Functions.

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How Getir reduced model training durations by 90% with Amazon SageMaker and AWS Batch

AWS Machine Learning Blog

Established in 2015, Getir has positioned itself as the trailblazer in the sphere of ultrafast grocery delivery. In this post, we explain how we built an end-to-end product category prediction pipeline to help commercial teams by using Amazon SageMaker and AWS Batch , reducing model training duration by 90%.

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Fast and cost-effective LLaMA 2 fine-tuning with AWS Trainium

AWS Machine Learning Blog

In this post, we walk through how to fine-tune Llama 2 on AWS Trainium , a purpose-built accelerator for LLM training, to reduce training times and costs. We review the fine-tuning scripts provided by the AWS Neuron SDK (using NeMo Megatron-LM), the various configurations we used, and the throughput results we saw.

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Federated Learning on AWS with FedML: Health analytics without sharing sensitive data – Part 2

AWS Machine Learning Blog

Because they’re in a highly regulated domain, HCLS partners and customers seek privacy-preserving mechanisms to manage and analyze large-scale, distributed, and sensitive data. To mitigate these challenges, we propose a federated learning (FL) framework, based on open-source FedML on AWS, which enables analyzing sensitive HCLS data.

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How Meesho built a generalized feed ranker using Amazon SageMaker inference

AWS Machine Learning Blog

Meesho was founded in 2015 and today focuses on buyers and sellers across India. We used AWS machine learning (ML) services like Amazon SageMaker to develop a powerful generalized feed ranker (GFR). In the following sections, we discuss each component and the AWS services used in more detail.

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How SnapLogic built a text-to-pipeline application with Amazon Bedrock to translate business intent into action

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In this post, we show you how SnapLogic , an AWS customer, used Amazon Bedrock to power their SnapGPT product through automated creation of these complex DSL artifacts from human language. SnapLogic background SnapLogic is an AWS customer on a mission to bring enterprise automation to the world.

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