AWS Machine Learning Blog

APRIL 29, 2024

For AWS and Outerbounds customers, the goal is to build a differentiated machine learning and artificial intelligence (ML/AI) system and reliably improve it over time. First, the AWS Trainium accelerator provides a high-performance, cost-effective, and readily available solution for training and fine-tuning large models.

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AWS Machine Learning Blog

DECEMBER 7, 2023

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. AWS Step Functions automatically initiate and monitor these workflows by simplifying error handling.

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AWS Machine Learning Blog

DECEMBER 4, 2023

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%. An important aspect of our strategy has been the use of SageMaker and AWS Batch to refine pre-trained BERT models for seven different languages.

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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

FEBRUARY 29, 2024

In this post, we investigate of potential for the AWS Graviton3 processor to accelerate neural network training for ThirdAI’s unique CPU-based deep learning engine. As shown in our results, we observed a significant training speedup with AWS Graviton3 over the comparable Intel and NVIDIA instances on several representative modeling workloads.

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AWS Machine Learning Blog

DECEMBER 12, 2023

In this post, we’ll summarize training procedure of GPT NeoX on AWS Trainium , a purpose-built machine learning (ML) accelerator optimized for deep learning training. M tokens/$) trained such models with AWS Trainium without losing any model quality. We’ll outline how we cost-effectively (3.2 billion in Pythia. 2048 256 10.4

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AWS Machine Learning Blog

JUNE 13, 2023

The size of the machine learning (ML) models––large language models ( LLMs ) and foundation models ( FMs )–– is growing fast year-over-year , and these models need faster and more powerful accelerators, especially for generative AI. With AWS Inferentia1, customers saw up to 2.3x AWS Inferentia2 has 4 times more memory capacity, 16.4

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AWS Machine Learning Blog

NOVEMBER 30, 2023

The number of companies launching generative AI applications on AWS is substantial and building quickly, including adidas, Booking.com, Bridgewater Associates, Clariant, Cox Automotive, GoDaddy, and LexisNexis Legal & Professional, to name just a few. Innovative startups like Perplexity AI are going all in on AWS for generative AI.

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AWS Machine Learning Blog

AUGUST 7, 2023

In an effort to create and maintain a socially responsible gaming environment, AWS Professional Services was asked to build a mechanism that detects inappropriate language (toxic speech) within online gaming player interactions. Unfortunately, as in the real world, not all players communicate appropriately and respectfully.

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AWS Machine Learning Blog

SEPTEMBER 19, 2023

Amazon SageMaker Feature Store provides an end-to-end solution to automate feature engineering for machine learning (ML). For many ML use cases, raw data like log files, sensor readings, or transaction records need to be transformed into meaningful features that are optimized for model training. SageMaker Studio set up.

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Data Science 101

NOVEMBER 11, 2019

SQL Server 2019 SQL Server 2019 went Generally Available. Data Science Announcements from Microsoft Ignite Many other services were announced such as: Azure Quantum, Project Silica, R support in Azure ML, and Visual Studio Online. If you are at a University or non-profit, you can ask for cash and/or AWS credits.

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Data Science 101

NOVEMBER 22, 2019

AWS Storage Day On November 20, 2019, Amazon held AWS Storage Day. Many announcements came out regarding storage of all types at AWS. Much of this is in anticipation of AWS re:Invent, coming in early December 2019. Much of this is in anticipation of AWS re:Invent, coming in early December 2019.

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AWS Machine Learning Blog

APRIL 9, 2024

In the following sections, we explain how you can use these features with either the AWS Management Console or SDK. The correct response for this query is “Amazon’s annual revenue increased from $245B in 2019 to $434B in 2022,” based on the documents in the knowledge base. We ask “What was the Amazon’s revenue in 2019 and 2021?”

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AWS Machine Learning Blog

DECEMBER 1, 2023

Launched in 2019, Amazon SageMaker Studio provides one place for all end-to-end machine learning (ML) workflows, from data preparation, building and experimentation, training, hosting, and monitoring. About the Authors Mair Hasco is an AI/ML Specialist for Amazon SageMaker Studio. Get started on SageMaker Studio here.

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AWS Machine Learning Blog

JULY 9, 2024

In the following example, for an LLM to answer the question correctly, it needs to understand the table row represents location and the column represents year, and then extract the correct quantity (total amount) from the table based on the asked location and year: Question : What was the Total Americas amount in 2019?

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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. These models are then pushed to an Amazon Simple Storage Service (Amazon S3) bucket using DVC, a version control tool for ML models. The DJL was created at Amazon and open-sourced in 2019.

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AWS Machine Learning Blog

FEBRUARY 19, 2024

Note that you can also use Knowledge Bases for Amazon Bedrock service APIs and the AWS Command Line Interface (AWS CLI) to programmatically create a knowledge base. Create a Lambda function This Lambda function is deployed using an AWS CloudFormation template available in the GitHub repo under the /cfn folder.

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AWS Machine Learning Blog

OCTOBER 2, 2023

It is architected to automate the entire machine learning (ML) process, from data labeling to model training and deployment at the edge. On top of that, the whole process can be configured and managed via the AWS SDK, which is what we use to orchestrate our labeling workflow as part of our CI/CD pipeline.

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AWS Machine Learning Blog

APRIL 19, 2024

We used AWS services including Amazon Bedrock , Amazon SageMaker , and Amazon OpenSearch Serverless in this solution. In this series, we use the slide deck Train and deploy Stable Diffusion using AWS Trainium & AWS Inferentia from the AWS Summit in Toronto, June 2023 to demonstrate the solution. I need numbers."

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AWS Machine Learning Blog

NOVEMBER 1, 2024

For our evaluation, we used the F1 score , which is an evaluation metric to assess the performance of LLMs and traditional ML models. Sovik Kumar Nath is an AI/ML and Generative AI Senior Solutions Architect with AWS. Sovik Kumar Nath is an AI/ML and Generative AI Senior Solutions Architect with AWS.

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AWS Machine Learning Blog

JULY 17, 2024

AWS announced the availability of the Cohere Command R fine-tuning model on Amazon SageMaker. This latest addition to the SageMaker suite of machine learning (ML) capabilities empowers enterprises to harness the power of large language models (LLMs) and unlock their full potential for a wide range of applications.

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AWS Machine Learning Blog

JULY 29, 2024

Amazon Forecast is a fully managed service that uses statistical and machine learning (ML) algorithms to deliver highly accurate time series forecasts. Benefits of SageMaker Canvas Forecast customers have been seeking greater transparency, lower costs, faster training, and enhanced controls for building time series ML models.

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AWS Machine Learning Blog

MARCH 28, 2024

It provides a collection of pre-trained models that you can deploy quickly and with ease, accelerating the development and deployment of machine learning (ML) applications. For more information on Mixtral-8x7B Instruct on AWS, refer to Mixtral-8x7B is now available in Amazon SageMaker JumpStart. license, for use without restrictions.

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AWS Machine Learning Blog

MAY 17, 2023

AWS delivers services that meet customers’ artificial intelligence (AI) and machine learning (ML) needs with services ranging from custom hardware like AWS Trainium and AWS Inferentia to generative AI foundation models (FMs) on Amazon Bedrock. Sub ${AWS::StackName}-SageMakerModel Containers: - Image: !Ref

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AWS Machine Learning Blog

FEBRUARY 24, 2023

AWS recently released Amazon SageMaker geospatial capabilities to provide you with satellite imagery and geospatial state-of-the-art machine learning (ML) models, reducing barriers for these types of use cases. For more information, refer to Preview: Use Amazon SageMaker to Build, Train, and Deploy ML Models Using Geospatial Data.

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AWS Machine Learning Blog

JULY 3, 2024

To answer this question, the AWS Generative AI Innovation Center recently developed an AI assistant for medical content generation. For this purpose, we use Amazon Textract, a machine learning (ML) service for entity recognition and extraction. 2019 Apr;179(4):561-569. Epub 2019 Jan 31. Am J Med Genet A. Int J Nurs Stud.

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AWS Machine Learning Blog

MARCH 14, 2024

In this post we highlight how the AWS Generative AI Innovation Center collaborated with the AWS Professional Services and PGA TOUR to develop a prototype virtual assistant using Amazon Bedrock that could enable fans to extract information about any event, player, hole or shot level details in a seamless interactive manner.

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AWS Machine Learning Blog

DECEMBER 29, 2022

This is why we launched Amazon Textract in 2019 to help you automate your tedious document processing workflows powered by AI. Integration with AWS Service Quotas. You can now proactively manage all your Amazon Textract service quotas via the AWS Service Quotas console. Increased default service quotas for Amazon Textract.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). ML is often associated with PBAs, so we start this post with an illustrative figure. The ML paradigm is learning followed by inference. The union of advances in hardware and ML has led us to the current day.

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AWS Machine Learning Blog

SEPTEMBER 14, 2023

“Data locked away in text, audio, social media, and other unstructured sources can be a competitive advantage for firms that figure out how to use it“ Only 18% of organizations in a 2019 survey by Deloitte reported being able to take advantage of unstructured data. The majority of data, between 80% and 90%, is unstructured data.

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AWS Machine Learning Blog

SEPTEMBER 28, 2023

Modern, state-of-the-art time series forecasting enables choice To meet real-world forecasting needs, AWS provides a broad and deep set of capabilities that deliver a modern approach to time series forecasting. AWS services address this need by the use of ML models coupled with quantile regression. References DeYong, G.

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AWS Machine Learning Blog

APRIL 4, 2023

AWS provides various services catered to time series data that are low code/no code, which both machine learning (ML) and non-ML practitioners can use for building ML solutions. We recommend running this notebook on Amazon SageMaker Studio , a web-based, integrated development environment (IDE) for ML.

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The MLOps Blog

MARCH 20, 2023

Long-term ML project involves developing and sustaining applications or systems that leverage machine learning models, algorithms, and techniques. An example of a long-term ML project will be a bank fraud detection system powered by ML models and algorithms for pattern recognition. 2 Ensuring and maintaining high-quality data.

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AWS Machine Learning Blog

SEPTEMBER 10, 2024

As part of its goal to help people live longer, healthier lives, Genomics England is interested in facilitating more accurate identification of cancer subtypes and severity, using machine learning (ML). We provide insights on interpretability, robustness, and best practices of architecting complex ML workflows on AWS with Amazon SageMaker.

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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 evaluated the WAPE for all BLs in the auto end market for 2019, 2020, and 2021.

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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. This labeling process is complex.

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Flipboard

FEBRUARY 2, 2023

With advanced analytics derived from machine learning (ML), the NFL is creating new ways to quantify football, and to provide fans with the tools needed to increase their knowledge of the games within the game of football. We then explain the details of the ML methodology and model training procedures.

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