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Deep Learning

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

AWS Machine Learning Blog

OCTOBER 5, 2023

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.

AWS

AWS Machine Learning Machine Learning Deep Learning

Build a medical imaging AI inference pipeline with MONAI Deploy on AWS

AWS Machine Learning Blog

NOVEMBER 8, 2023

AWS and NVIDIA have come together to make this vision a reality. AWS, NVIDIA, and other partners build applications and solutions to make healthcare more accessible, affordable, and efficient by accelerating cloud connectivity of enterprise imaging. AHI provides API access to ImageSet metadata and ImageFrames.

AWS

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A Glimpse into the Unprecedented Growth of NVIDIA in the World of AI

Data Science Dojo

MARCH 4, 2024

Emerging as a key player in deep learning (2010s) The decade was marked by focusing on deep learning and navigating the potential of AI. Introduction of cuDNN Library: In 2014, the company launched its cuDNN (CUDA Deep Neural Network) Library. It provided optimized codes for deep learning models.

Deep Learning

Deep Learning Deep Learning AI AI

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Maximizing Profit and Productivity: The New Era of AI-Powered Accounting

Automation, Evolved: Your New Playbook For Smarter Knowledge Work

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Rad AI reduces real-time inference latency by 50% using Amazon SageMaker

AWS Machine Learning Blog

NOVEMBER 26, 2024

Since 2018, using state-of-the-art proprietary and open source large language models (LLMs), our flagship product— Rad AI Impressions — has significantly reduced the time radiologists spend dictating reports, by generating Impression sections. This post is co-written with Ken Kao and Hasan Ali Demirci from Rad AI.

ML ML AI AI

Llama 4 family of models from Meta are now available in SageMaker JumpStart

AWS Machine Learning Blog

APRIL 7, 2025

Virginia) AWS Region. Prerequisites To try the Llama 4 models in SageMaker JumpStart, you need the following prerequisites: An AWS account that will contain all your AWS resources. An AWS Identity and Access Management (IAM) role to access SageMaker AI. The example extracts and contextualizes the buildspec-1-10-2.yml

AWS

AWS Machine Learning Machine Learning ML

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

AWS Machine Learning Blog

JANUARY 13, 2023

To mitigate these challenges, we propose a federated learning (FL) framework, based on open-source FedML on AWS, which enables analyzing sensitive HCLS data. It involves training a global machine learning (ML) model from distributed health data held locally at different sites. Request a VPC peering connection.

AWS

AWS Analytics Analytics Machine Learning

How Sportradar used the Deep Java Library to build production-scale ML platforms for increased performance and efficiency

AWS Machine Learning Blog

APRIL 19, 2023

The DJL is a deep learning framework built from the ground up to support users of Java and JVM languages like Scala, Kotlin, and Clojure. With the DJL, integrating this deep learning is simple. Since 2018, our team has been developing a variety of ML models to enable betting products for NFL and NCAA football.

ML ML Deep Learning Deep Learning

A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

SEPTEMBER 11, 2024

In 2018, other forms of PBAs became available, and by 2020, PBAs were being widely used for parallel problems, such as training of NN. Examples of other PBAs now available include AWS Inferentia and AWS Trainium , Google TPU, and Graphcore IPU. Thirdly, the presence of GPUs enabled the labeled data to be processed.

AWS

AWS ML ML Clustering

Enhancing Just Walk Out technology with multi-modal AI

AWS Machine Learning Blog

SEPTEMBER 23, 2024

Since its launch in 2018, Just Walk Out technology by Amazon has transformed the shopping experience by allowing customers to enter a store, pick up items, and leave without standing in line to pay. Learn more about how to power your store or venue with Just Walk Out technology by Amazon on the Just Walk Out technology product page.

AI AI AWS Algorithm

Generate a counterfactual analysis of corn response to nitrogen with Amazon SageMaker JumpStart solutions

AWS Machine Learning Blog

APRIL 3, 2023

The accomplishments of deep learning are essentially just a type of curve ﬁtting, whereas causality could be used to uncover interactions between the systems of the world under various constraints without testing hypotheses directly. Prerequisites You need an AWS account to use this solution.

Database

Database AWS Machine Learning Machine Learning

Predicting new and existing product sales in semiconductors using Amazon Forecast

AWS Machine Learning Blog

APRIL 6, 2023

& AWS Machine Learning Solutions Lab (MLSL) Machine learning (ML) is being used across a wide range of industries to extract actionable insights from data to streamline processes and improve revenue generation. We trained three models using data from 2011–2018 and predicted the sales values until 2021.

Machine Learning

Machine Learning Machine Learning ML ML

Train self-supervised vision transformers on overhead imagery with Amazon SageMaker

AWS Machine Learning Blog

AUGUST 16, 2023

The images document the land cover, or physical surface features, of ten European countries between June 2017 and May 2018. Because we use true color images during DINO training, we only upload the red (B04), green (B03), and blue (B02) bands: aws s3 cp final_ben_s2.parquet Machine Learning Engineer at AWS.

ML ML Data Scientist AWS

Identifying defense coverage schemes in NFL’s Next Gen Stats

AWS Machine Learning Blog

FEBRUARY 10, 2023

Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation. Mohamad Al Jazaery is an applied scientist at Amazon Machine Learning Solutions Lab. Prior to AWS, he obtained his MCS from West Virginia University and worked as computer vision researcher at Midea.

ML ML Machine Learning Machine Learning

Effectively solve distributed training convergence issues with Amazon SageMaker Hyperband Automatic Model Tuning

AWS Machine Learning Blog

JULY 13, 2023

Recent years have shown amazing growth in deep learning neural networks (DNNs). International Conference on Machine Learning. PMLR, 2018. [2] On large-batch training for deep learning: Generalization gap and sharp minima.” Toward understanding the impact of staleness in distributed machine learning.”

Clustering

Clustering Algorithm ML ML

Question answering using Retrieval Augmented Generation with foundation models in Amazon SageMaker JumpStart

AWS Machine Learning Blog

MAY 2, 2023

There are a few limitations of using off-the-shelf pre-trained LLMs: They’re usually trained offline, making the model agnostic to the latest information (for example, a chatbot trained from 2011–2018 has no information about COVID-19). Managed Spot Training is supported in all AWS Regions where Amazon SageMaker is currently available.

Algorithm

Algorithm Machine Learning Machine Learning Natural Language Processing

Fine-tune and deploy Llama 2 models cost-effectively in Amazon SageMaker JumpStart with AWS Inferentia and AWS Trainium

AWS Machine Learning Blog

JANUARY 17, 2024

Today, we’re excited to announce the availability of Llama 2 inference and fine-tuning support on AWS Trainium and AWS Inferentia instances in Amazon SageMaker JumpStart. In this post, we demonstrate how to deploy and fine-tune Llama 2 on Trainium and AWS Inferentia instances in SageMaker JumpStart.

AWS

AWS Python Machine Learning Machine Learning

Announcing New Tools for Building with Generative AI on AWS

Flipboard

APRIL 13, 2023

Prime Air (our drones) and the computer vision technology in Amazon Go (our physical retail experience that lets consumers select items off a shelf and leave the store without having to formally check out) use deep learning. AWS has the broadest and deepest portfolio of AI and ML services at all three layers of the stack.

AWS

AWS AI ML ML

Accelerating large-scale neural network training on CPUs with ThirdAI and AWS Graviton

AWS Machine Learning Blog

FEBRUARY 29, 2024

Large-scale deep learning has recently produced revolutionary advances in a vast array of fields. is a startup dedicated to the mission of democratizing artificial intelligence technologies through algorithmic and software innovations that fundamentally change the economics of deep learning. Founded in 2021, ThirdAI Corp.

AWS

AWS Deep Learning Deep Learning ML

Fast and accurate zero-shot forecasting with Chronos-Bolt and AutoGluon

AWS Machine Learning Blog

DECEMBER 2, 2024

Over the past decade, advancements in deep learning have spurred a shift toward so-called global models such as DeepAR [3] and PatchTST [4]. AutoGluon predictors can be seamlessly deployed to SageMaker using AutoGluon-Cloud and the official Deep Learning Containers. Chronos: Learning the language of time series.”

Machine Learning

Machine Learning Machine Learning Deep Learning Deep Learning

Using LLMs to fortify cyber defenses: Sophos’s insight on strategies for using LLMs with Amazon Bedrock and Amazon SageMaker

AWS Machine Learning Blog

NOVEMBER 26, 2024

About the Authors Benoit de Patoul is a GenAI/AI/ML Specialist Solutions Architect at AWS. Naresh Nagpal is a Solutions Architect at AWS with extensive experience in application development, integration, and technology architecture. In his free time, he likes to play piano and spend time with friends.

Machine Learning

Machine Learning Machine Learning SQL ML

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

Build a medical imaging AI inference pipeline with MONAI Deploy on AWS

Webinars

Trending Sources

A Glimpse into the Unprecedented Growth of NVIDIA in the World of AI

Webinars

Rad AI reduces real-time inference latency by 50% using Amazon SageMaker

Llama 4 family of models from Meta are now available in SageMaker JumpStart

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

How Sportradar used the Deep Java Library to build production-scale ML platforms for increased performance and efficiency

A review of purpose-built accelerators for financial services

Enhancing Just Walk Out technology with multi-modal AI

Generate a counterfactual analysis of corn response to nitrogen with Amazon SageMaker JumpStart solutions

Predicting new and existing product sales in semiconductors using Amazon Forecast

Train self-supervised vision transformers on overhead imagery with Amazon SageMaker

Identifying defense coverage schemes in NFL’s Next Gen Stats

Effectively solve distributed training convergence issues with Amazon SageMaker Hyperband Automatic Model Tuning

Question answering using Retrieval Augmented Generation with foundation models in Amazon SageMaker JumpStart

Fine-tune and deploy Llama 2 models cost-effectively in Amazon SageMaker JumpStart with AWS Inferentia and AWS Trainium

Announcing New Tools for Building with Generative AI on AWS

Accelerating large-scale neural network training on CPUs with ThirdAI and AWS Graviton

Fast and accurate zero-shot forecasting with Chronos-Bolt and AutoGluon

Using LLMs to fortify cyber defenses: Sophos’s insight on strategies for using LLMs with Amazon Bedrock and Amazon SageMaker

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