AWS Machine Learning Blog

JUNE 26, 2024

This post is a follow-up to Generative AI and multi-modal agents in AWS: The key to unlocking new value in financial markets. Technical architecture and key steps The multi-modal agent orchestrates various steps based on natural language prompts from business users to generate insights.

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AWS Machine Learning Blog

FEBRUARY 6, 2024

We implemented the solution using the AWS Cloud Development Kit (AWS CDK). Transformers, BERT, and GPT The transformer architecture is a neural network architecture that is used for natural language processing (NLP) tasks. The first GPT model was introduced in 2018 by OpenAI.

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AWS Machine Learning Blog

NOVEMBER 9, 2023

In these two studies, commissioned by AWS, developers were asked to create a medical software application in Java that required use of their internal libraries. About the authors Qing Sun is a Senior Applied Scientist in AWS AI Labs and work on AWS CodeWhisperer, a generative AI-powered coding assistant.

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AWS Machine Learning Blog

MARCH 14, 2024

Amazon Kendra uses natural language processing (NLP) to understand user queries and find the most relevant documents. The following figures shows the step-by-step procedure of how a query is processed for the text-to-SQL pipeline. This will require extensive testing, through collaboration between AWS and the PGA TOUR.

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Dataconomy

SEPTEMBER 4, 2023

It uses natural language processing (NLP) techniques to extract valuable insights from textual data. For instance, British Airways faced a fine of £183 million ($230 million) for a GDPR breach in 2018. Downtime, like the AWS outage in 2017 that affected several high-profile websites, can disrupt business operations.

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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. In November 2023, AWS announced the next generation Trainium2 chip.

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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. tif" --include "_B03.tif"

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AWS Machine Learning Blog

OCTOBER 2, 2024

Additionally, check out the service introduction video from AWS re:Invent 2023. About the Authors Maira Ladeira Tanke is a Senior Generative AI Data Scientist at AWS. Mark Roy is a Principal Machine Learning Architect for AWS, helping customers design and build generative AI solutions.

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AWS Machine Learning Blog

APRIL 19, 2023

Since 2018, our team has been developing a variety of ML models to enable betting products for NFL and NCAA football. It has intuitive helpers and utilities for modalities like computer vision, natural language processing, audio, time series, and tabular data. We recently developed four more new models.

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AWS Machine Learning Blog

JUNE 5, 2023

In this post, we show you how to train the 7-billion-parameter BloomZ model using just a single graphics processing unit (GPU) on Amazon SageMaker , Amazon’s machine learning (ML) platform for preparing, building, training, and deploying high-quality ML models. BloomZ is a general-purpose natural language processing (NLP) model.

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Chatbots Life

MAY 16, 2023

Master of Code Global (MOCG) is a certified partner of Microsoft and AWS and has been recognized by LivePerson, Inc. Data Monsters can help companies deploy, train and test machine learning pipelines for natural language processing and computer vision. Elite Service Delivery partner of NVIDIA.

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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. Prior to AWS, he obtained his MCS from West Virginia University and worked as computer vision researcher at Midea. He is broadly interested in Deep Learning and Natural Language Processing.

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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.

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AWS Machine Learning Blog

MAY 22, 2023

Prerequisites To get started, all you need is an AWS account in which you can use Studio. It develops insights by recognizing the entities, key phrases, language, sentiments, and other common elements in a document. Baris Kurt is an Applied Scientist at AWS AI Labs. Jonas Kübler is an Applied Scientist at AWS AI Labs.

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AWS Machine Learning Blog

FEBRUARY 8, 2023

About the Authors Mohan Gandhi is a Senior Software Engineer at AWS. He has been with AWS for the last 10 years and has worked on various AWS services like EMR, EFA and RDS. He is currently focused on natural language processing, responsible AI, inference optimization and scaling ML across the enterprise.

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The MLOps Blog

JULY 18, 2023

First and foremost, let’s say that we have some parts of our stack, especially the audio componentry, that tend to require heavy GPU machines to operate some of the pure language side of the house, such as the natural language processing model. Some of them can be handled purely on CPU processing.

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Snorkel AI

MAY 25, 2023

A brief history of large language models Large language models grew out of research and experiments with neural networks to allow computers to process natural language. From 2018 to the modern day, NLP researchers have engaged in a steady march toward ever-larger models. For example: I love this movie.

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Snorkel AI

MAY 25, 2023

A brief history of large language models Large language models grew out of research and experiments with neural networks to allow computers to process natural language. From 2018 to the modern day, NLP researchers have engaged in a steady march toward ever-larger models. For example: I love this movie.

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Explosion

AUGUST 1, 2019

Transformers and transfer-learning Natural Language Processing (NLP) systems face a problem known as the “knowledge acquisition bottleneck”. Based on the (fairly vague) marketing copy, AWS might be doing something similar in SageMaker. We have updated our library and this blog post accordingly. and follows Devlin et al.

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Flipboard

JANUARY 14, 2025

The research team at AWS has worked extensively on building and evaluating the multi-agent collaboration (MAC) framework so customers can orchestrate multiple AI agents on Amazon Bedrock Agents. At AWS, he led the Dialog2API project, which enables large language models to interact with the external environment through dialogue.

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Flipboard

APRIL 16, 2025

Solution overview The AI-powered asset inventory labeling solution aims to streamline the process of updating inventory databases by automatically extracting relevant information from asset labels through computer vision and generative AI capabilities. LLMs are large deep learning models that are pre-trained on vast amounts of data.

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AWS Machine Learning Blog

OCTOBER 21, 2024

Amazon Bedrock Agents allows you to write IaC code with AWS CloudFormation , the AWS Cloud Development Kit (AWS CDK), or Terraform. We provide blueprint templates of the most common capabilities of Amazon Bedrock Agents, which can be deployed and updated with a single AWS CDK command.

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AWS Machine Learning Blog

NOVEMBER 11, 2024

Prerequisites To try out this solution using SageMaker JumpStart, you’ll need the following prerequisites: An AWS account that will contain all of your AWS resources. An AWS Identity and Access Management (IAM) role to access SageMaker. He is specialized in architecting AI/ML and generative AI services at AWS.

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