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

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AWS Machine Learning Blog

OCTOBER 16, 2024

See Amazon SageMaker geospatial capabilities to learn more. About the Author Xiong Zhou is a Senior Applied Scientist at AWS. He leads the science team for Amazon SageMaker geospatial capabilities. Janosch Woschitz is a Senior Solutions Architect at AWS, specializing in AI/ML. He is an ACM Fellow and IEEE Fellow.

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AWS Machine Learning Blog

JUNE 27, 2024

In this post, we describe how we built our cutting-edge productivity agent NinjaLLM, the backbone of MyNinja.ai, using AWS Trainium chips. We also used AWS ParallelCluster to manage cluster orchestration. For training, we chose to use a cluster of trn1.32xlarge instances to take advantage of Trainium chips. Arash co-founded Ninjatech.ai

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AWS Machine Learning Blog

MAY 1, 2024

Llama2 by Meta is an example of an LLM offered by AWS. To learn more about Llama 2 on AWS, refer to Llama 2 foundation models from Meta are now available in Amazon SageMaker JumpStart. Virginia) and US West (Oregon) AWS Regions, and most recently announced general availability in the US East (Ohio) Region.

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MAY 15, 2025

The content and opinions in this post are those of the third-party author and AWS is not responsible for the content or accuracy of this post. AI at Qualtrics Qualtrics has a deep history of using advanced ML to power its industry-leading experience management platform. Qualtrics refers to it internally as the Socrates platform.

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insideBIGDATA

JANUARY 16, 2024

In this contributed article, Stefano Soatto, Professor of Computer Science at the University of California, Los Angeles and a Vice President at Amazon Web Services, discusses generative AI models and how they are designed and trained to hallucinate, so hallucinations are a common product of any generative model.

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DECEMBER 2, 2024

Today at AWS re:Invent 2024, we are excited to announce a new feature for Amazon SageMaker inference endpoints: the ability to scale SageMaker inference endpoints to zero instances. This long-awaited capability is a game changer for our customers using the power of AI and machine learning (ML) inference in the cloud.

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AWS Machine Learning Blog

JUNE 25, 2024

Amazon Web Services is excited to announce the launch of the AWS Neuron Monitor container , an innovative tool designed to enhance the monitoring capabilities of AWS Inferentia and AWS Trainium chips on Amazon Elastic Kubernetes Service (Amazon EKS).

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AWS Machine Learning Blog

JANUARY 13, 2023

In this two-part series, we demonstrate how you can deploy a cloud-based FL framework on AWS. We have developed an FL framework on AWS that enables analyzing distributed and sensitive health data in a privacy-preserving manner. In this post, we showed how you can deploy the open-source FedML framework on AWS. Conclusion.

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AWS Machine Learning Blog

OCTOBER 23, 2024

SnapLogic uses Amazon Bedrock to build its platform, capitalizing on the proximity to data already stored in Amazon Web Services (AWS). To address customers’ requirements about data privacy and sovereignty, SnapLogic deploys the data plane within the customer’s VPC on AWS.

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AWS Machine Learning Blog

SEPTEMBER 9, 2024

To address customer needs for high performance and scalability in deep learning, generative AI, and HPC workloads, we are happy to announce the general availability of Amazon Elastic Compute Cloud (Amazon EC2) P5e instances, powered by NVIDIA H200 Tensor Core GPUs. Karthik Venna is a Principal Product Manager at AWS.

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AWS Machine Learning Blog

MAY 10, 2023

Project Jupyter is a multi-stakeholder, open-source project that builds applications, open standards, and tools for data science, machine learning (ML), and computational science. Given the importance of Jupyter to data scientists and ML developers, AWS is an active sponsor and contributor to Project Jupyter.

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AWS Machine Learning Blog

MAY 15, 2025

By using cutting-edge generative AI and deep learning technologies, Apoidea has developed innovative AI-powered solutions that address the unique needs of multinational banks. About the Authors Tony Wong is a Solutions Architect at AWS based in Hong Kong, specializing in financial services.

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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.

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AWS Machine Learning Blog

JUNE 18, 2024

Some examples include extracting players and positions in an NFL game summary, products mentioned in an AWS keynote transcript, or key names from an article on a favorite tech company. We extract the default generic entities through the AWS SDK for Python (Boto3) as follows: import pandas as pd comprehend_client = boto3.client("comprehend")

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AWS Machine Learning Blog

JUNE 7, 2023

Libraries such as DeepSpeed (an open-source deep learning optimization library for PyTorch) address some of these challenges, and can help accelerate model development and training. Training setup We provisioned a managed compute cluster comprised of 16 dl1.24xlarge instances using AWS Batch.

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ODSC - Open Data Science

FEBRUARY 17, 2023

Developing NLP tools isn’t so straightforward, and requires a lot of background knowledge in machine & deep learning, among others. Machine & Deep Learning Machine learning is the fundamental data science skillset, and deep learning is the foundation for NLP.

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Towards AI

JANUARY 6, 2025

Professional certificate for computer science for AI by HARVARD UNIVERSITY Professional certificate for computer science for AI is a 5-month AI course that is inclusive of self-paced videos for participants; who are beginners or possess intermediate-level understanding of artificial intelligence.

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

In terms of security, both the input and output are secured using TLS using AWS Sigv4 Auth. In this post, we showcase two container options to create a SageMaker endpoint with response streaming: using an AWS Large Model Inference (LMI) and Hugging Face Text Generation Inference (TGI) container.

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AWS Machine Learning Blog

SEPTEMBER 13, 2024

Multimodal is a type of deep learning using multiple modalities of data, such as text, audio, or images. This feature is available in all AWS Regions where SageMaker is available. To learn more about deploying models on SageMaker, see Amazon SageMaker Model Deployment. gpu-py311-cu121-ubuntu20.04-sagemaker.

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AWS Machine Learning Blog

SEPTEMBER 6, 2023

This feature is particularly beneficial for deep learning and generative AI models that require accelerated compute. The cost savings achieved through resource sharing and simplified model management makes SageMaker MMEs an excellent choice for you to host models at scale on AWS.

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AWS Machine Learning Blog

APRIL 18, 2023

MLSL’s high caliber talent, culture, and focus on aiding our realization of measurable and compelling results from machine learning investments enabled us to reduce suicide risk, improve career transition, and speed up important connections for our service members, veterans, and their families.” Applied AI Specialist Architect at AWS.

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AWS Machine Learning Blog

MARCH 8, 2023

The workflow steps are as follows: Set up a SageMaker notebook and an AWS Identity and Access Management (IAM) role with appropriate permissions to allow SageMaker to access Amazon Elastic Container Registry (Amazon ECR), Secrets Manager, and other services within your AWS account. AWS Region Link us-east-1 (N.

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AWS Machine Learning Blog

AUGUST 29, 2024

Companies like Amgen , A-Alpha Bio , Agilent , and Hippocratic AI are among those using NVIDIA AI on AWS to accelerate computational biology, genomics analysis, and conversational AI. aws ecr describe-repositories --repository-names "${nim_image}" --region "${region}" > /dev/null 2>&1 if [ $? -ne dkr.ecr." -ne

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AWS Machine Learning Blog

MAY 31, 2024

Falcon 2 11B is supported by the SageMaker TGI Deep Learning Container (DLC) which is powered by Text Generation Inference (TGI) , an open source, purpose-built solution for deploying and serving LLMs that enables high-performance text generation using tensor parallelism and dynamic batching. Avan Bala is a Solutions Architect at AWS.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

The compute clusters used in these scenarios are composed of more than thousands of AI accelerators such as GPUs or AWS Trainium and AWS Inferentia , custom machine learning (ML) chips designed by Amazon Web Services (AWS) to accelerate deep learning workloads in the cloud. at a minimum).

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AWS Machine Learning Blog

SEPTEMBER 22, 2023

For more information, refer to the AWS Sagemaker Developer Guide’s documentation on “ Clean Up ”. About the Authors Weifeng Chen is an Applied Scientist in the AWS Human-in-the-loop science team. Erran Li is the applied science manager at humain-in-the-loop services, AWS AI, Amazon.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

Examples of other PBAs now available include AWS Inferentia and AWS Trainium , Google TPU, and Graphcore IPU. Around this time, industry observers reported NVIDIA’s strategy pivoting from its traditional gaming and graphics focus to moving into scientific computing and data analytics.

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ODSC - Open Data Science

FEBRUARY 2, 2023

Just as a writer needs to know core skills like sentence structure, grammar, and so on, data scientists at all levels should know core data science skills like programming, computer science, algorithms, and so on. They’re looking for people who know all related skills, and have studied computer science and software engineering.

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AWS Machine Learning Blog

OCTOBER 27, 2023

AWS AI services, such as Amazon Personalize and Amazon Bedrock, can help recommend and deliver products, content, and compelling marketing messages personalized to your users. For more information on working with generative AI on AWS, see to Announcing New Tools for Building with Generative AI on AWS. Jingwen Hu is a Sr.

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AUGUST 17, 2023

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. Prerequisites To continue with the examples in this post, you need to create the required AWS resources.

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AWS Machine Learning Blog

OCTOBER 17, 2023

The Amazon Personalize Search Ranking plugin within OpenSearch Service allows you to improve the end-user engagement and conversion from your website and app search by taking advantage of the deep learning capabilities offered by Amazon Personalize. Technical Product Manager working with AWS AI/ML on the Amazon Personalize team.

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AWS Machine Learning Blog

SEPTEMBER 8, 2023

This post is co-written with Travis Bronson, and Brian L Wilkerson from Duke Energy Machine learning (ML) is transforming every industry, process, and business, but the path to success is not always straightforward. We evaluated the performance of two AWS services Amazon Rekognition and Amazon Lookout for Vision.

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AWS Machine Learning Blog

AUGUST 9, 2024

This post provides an overview of a custom solution developed by the AWS Generative AI Innovation Center (GenAIIC) for Deltek , a globally recognized standard for project-based businesses in both government contracting and professional services. For technical support or to contact AWS generative AI specialists, visit the GenAIIC webpage.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

As organizations deploy models to production, they are constantly looking for ways to optimize the performance of their foundation models (FMs) running on the latest accelerators, such as AWS Inferentia and GPUs, so they can reduce their costs and decrease response latency to provide the best experience to end-users.

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AWS Machine Learning Blog

SEPTEMBER 12, 2023

Solution overview Scalable Capital’s ML infrastructure consists of two AWS accounts: one as an environment for the development stage and the other one for the production stage. The following diagram shows the workflow for our email classifier project, but can also be generalized to other data science projects. Use Version 2.x

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NOVEMBER 30, 2023

Although it provides various entry points like the SageMaker Python SDK, AWS SDKs, the SageMaker console, and Amazon SageMaker Studio notebooks to simplify the process of training and deploying ML models at scale, customers are still looking for better ways to deploy their models for playground testing and to optimize production deployments.

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