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.

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AWS Machine Learning Blog

SEPTEMBER 21, 2023

In this post, we discuss how United Airlines, in collaboration with the Amazon Machine Learning Solutions Lab , build an active learning framework on AWS to automate the processing of passenger documents. “In The process relies on manual annotations to train ML models, which are very costly.

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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.

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AWS Machine Learning Blog

APRIL 18, 2023

To improve this experience for its members, we at RallyPoint wanted to explore how machine learning (ML) could help. The following sections cover the business and technical challenges, the approach taken by the AWS and RallyPoint teams, and the performance of implemented solution that leverages Amazon Personalize.

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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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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

SEPTEMBER 6, 2023

Multi-model endpoints (MMEs) are a powerful feature of Amazon SageMaker designed to simplify the deployment and operation of machine learning (ML) models. This feature is particularly beneficial for deep learning and generative AI models that require accelerated compute.

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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

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

MAY 29, 2024

DeepSpeed is a library that helps train very large deep learning models faster and more efficiently. Reference LLaVA Visual Instruction Tuning (pdf) About the Authors Dr. Changsha Ma is an AI/ML Specialist at AWS. Jun Shi is a Senior Solutions Architect at Amazon Web Services (AWS).

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AWS Machine Learning Blog

MARCH 30, 2023

In this post, we introduce a new interactive tool called Snapper, powered by a machine learning (ML) model that reduces the effort required of annotators. ML model optimized for annotators A tremendous number of high-performing object detection models have been proposed by the computer vision community in recent years.

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AWS Machine Learning Blog

OCTOBER 27, 2023

Developers can use Amazon Personalize to build applications powered by the same type of machine learning (ML) technology used by Amazon.com for real-time personalized recommendations. With Amazon Personalize, developers can improve user engagement through personalized product and content recommendations with no ML expertise required.

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AWS Machine Learning Blog

MARCH 29, 2023

there is enormous potential to use machine learning (ML) for quality prediction. ML-based predictive quality in HAYAT HOLDING HAYAT is the world’s fourth-largest branded baby diapers manufacturer and the largest paper tissue manufacturer of the EMEA. With the ongoing digitization of the manufacturing processes and Industry 4.0,

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AWS Machine Learning Blog

MAY 2, 2023

SageMaker provides single model endpoints , which allow you to deploy a single machine learning (ML) model against a logical endpoint. Triton supports various backends as engines to support the running and serving of various ML models for inference. Set up the environment We begin by setting up the required environment.

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The MLOps Blog

OCTOBER 3, 2023

This article was originally an episode of the ML Platform Podcast , a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals. I really enjoyed it.

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AWS Machine Learning Blog

APRIL 25, 2023

Data scientists typically carry out several iterations of experimentation in data processing and training models while working on any ML problem. They want to run this ML code and carry out the experimentation with ease of use and minimal code change.

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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. In this blog post, we demonstrate how Duke Energy , a Fortune 150 company headquartered in Charlotte, NC.,

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AWS Machine Learning Blog

MARCH 13, 2023

Amazon SageMaker Ground Truth Plus is a managed data labeling service that makes it easy to label data for machine learning (ML) applications. One common use case is semantic segmentation, which is a computer vision ML technique that involves assigning class labels to individual pixels in an image.

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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

NOVEMBER 26, 2023

Amazon Personalize is excited to announce the new Next Best Action ( aws-next-best-action ) recipe to help you determine the best actions to suggest to your individual users that will enable you to increase brand loyalty and conversion. All your data is encrypted to be private and secure. and value of that action, if provided.

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AWS Machine Learning Blog

SEPTEMBER 12, 2023

The machine learning (ML) model classifies new incoming customer requests as soon as they arrive and redirects them to predefined queues, which allows our dedicated client success agents to focus on the contents of the emails according to their skills and provide appropriate responses. Use Version 2.x

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AWS Machine Learning Blog

OCTOBER 6, 2023

Background of multimodality models Machine learning (ML) models have achieved significant advancements in fields like natural language processing (NLP) and computer vision, where models can exhibit human-like performance in analyzing and generating content from a single source of data.

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Snorkel AI

MAY 24, 2023

From generative modeling to automated product tagging, cloud computing, predictive analytics, and deep learning, the speakers present a diverse range of expertise. He is also a professor emeritus of computer science at Stanford University, where he taught and researched since 1987.

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Snorkel AI

MAY 24, 2023

From generative modeling to automated product tagging, cloud computing, predictive analytics, and deep learning, the speakers present a diverse range of expertise. He is also a professor emeritus of computer science at Stanford University, where he taught and researched since 1987.

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AWS Machine Learning Blog

AUGUST 14, 2023

SageMaker JumpStart SageMaker JumpStart serves as a model hub encapsulating a broad array of deep learning models for text, vision, audio, and embedding use cases. With over 500 models, its model hub comprises both public and proprietary models from AWS’s partners such as AI21, Stability AI, Cohere, and LightOn.

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O'Reilly Media

OCTOBER 19, 2021

This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. What does a modern technology stack for streamlined ML processes look like?

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ODSC - Open Data Science

SEPTEMBER 22, 2023

Most recently, Gaurav served as VP of Product at Neural Magic — innovators in software acceleration for deep learning utilizing sparse model architectures. He is an MIT graduate in Computer Science and Physics, as well as a former software engineer at Palantir Technologies. Army’s first deployment of 3G and 4G networks.

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AWS Machine Learning Blog

NOVEMBER 1, 2023

Utilizing the latest Hugging Face LLM modules on Amazon SageMaker, AWS customers can now tap into the power of SageMaker deep learning containers (DLCs). About the authors Alfred Shen is a Senior AI/ML Specialist at AWS. He is a dedicated applied AI/ML researcher, concentrating on CV, NLP, and multimodality.

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ODSC - Open Data Science

MAY 1, 2023

.` Hagay Lupesko VP Engineering, MosaicML | Expert in Generative AI Training and Inference, Former Leader at Meta AI and AWS In his role as VP of Engineering, Hagay Lupesko focuses on making generative AI training and inference efficient, fast, and accessible. Audrey Reznik Guidera Sr.

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AWS Machine Learning Blog

MAY 30, 2023

From there, an expert workforce that is trained on a variety of machine learning (ML) tasks labels your data. You don’t even need deep ML expertise or knowledge of workflow design and quality management to use Ground Truth Plus. Prior to AWS she worked in various engineering management roles at Oracle and Sun Microsystem.

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AWS Machine Learning Blog

FEBRUARY 10, 2023

Through a collaboration between the Next Gen Stats team and the Amazon ML Solutions Lab , we have developed the machine learning (ML)-powered stat of coverage classification that accurately identifies the defense coverage scheme based on the player tracking data. Outside of work, he enjoys soccer and video games.

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AWS Machine Learning Blog

FEBRUARY 9, 2023

SageMaker now supports geospatial machine learning (ML), making it easier for data scientists and ML engineers to build, train, and deploy models using geospatial data. In this post, we showed how to acquire data, perform analysis, and visualize the changes with SageMaker geospatial AI/ML services.

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Flipboard

OCTOBER 9, 2023

This lesson is the 2nd of a 3-part series on Docker for Machine Learning : Getting Started with Docker for Machine Learning Getting Used to Docker for Machine Learning (this tutorial) Lesson 3 To learn how to create a Docker Container for Machine Learning, just keep reading. Join me in computer vision mastery.

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AWS Machine Learning Blog

APRIL 27, 2023

About the Authors Erran Li is the applied science manager at humain-in-the-loop services, AWS AI, Amazon. His research interests are 3D deep learning, and vision and language representation learning. He has a PhD in computer science at Cornell University. He is an ACM Fellow and IEEE Fellow.

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DrivenData Labs

JUNE 14, 2023

Yi Yang is a Professor with the college of computer science and technology, Zhejiang University. Our strong interest on deep metric learning motivated us to compete in this challenge. We believe VSC 2022 makes good social impact and is of great value to the deep learning research community.

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AWS Machine Learning Blog

APRIL 18, 2023

One of the major challenges in training and deploying LLMs with billions of parameters is their size, which can make it difficult to fit them into single GPUs, the hardware commonly used for deep learning. Defining hyperparameters involves setting the values for various parameters used during the training process of an ML model.

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