AWS Machine Learning Blog

DECEMBER 24, 2024

To simplify infrastructure setup and accelerate distributed training, AWS introduced Amazon SageMaker HyperPod in late 2023. In this blog post, we showcase how you can perform efficient supervised fine tuning for a Meta Llama 3 model using PEFT on AWS Trainium with SageMaker HyperPod. architectures/5.sagemaker-hyperpod/LifecycleScripts/base-config/

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AWS Machine Learning Blog

FEBRUARY 21, 2025

The Hadoop environment was hosted on Amazon Elastic Compute Cloud (Amazon EC2) servers, managed in-house by Rockets technology team, while the data science experience infrastructure was hosted on premises. Communication between the two systems was established through Kerberized Apache Livy (HTTPS) connections over AWS PrivateLink.

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AWS Machine Learning Blog

DECEMBER 12, 2024

The new SDK is designed with a tiered user experience in mind, where the new lower-level SDK ( SageMaker Core ) provides access to full breadth of SageMaker features and configurations, allowing for greater flexibility and control for ML engineers. Admins and users can also overwrite the defaults using the SDK defaults configuration file.

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AWS Machine Learning Blog

OCTOBER 24, 2024

Machine learning (ML) helps organizations to increase revenue, drive business growth, and reduce costs by optimizing core business functions such as supply and demand forecasting, customer churn prediction, credit risk scoring, pricing, predicting late shipments, and many others. Let’s learn about the services we will use to make this happen.

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AWS Machine Learning Blog

OCTOBER 29, 2024

Refer to Supported Regions and models for batch inference for current supporting AWS Regions and models. To address this consideration and enhance your use of batch inference, we’ve developed a scalable solution using AWS Lambda and Amazon DynamoDB. Amazon S3 invokes the {stack_name}-create-batch-queue-{AWS-Region} Lambda function.

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AWS Machine Learning Blog

OCTOBER 16, 2024

Amazon SageMaker supports geospatial machine learning (ML) capabilities, allowing data scientists and ML engineers to build, train, and deploy ML models using geospatial data. Identify areas of interest We begin by illustrating how SageMaker can be applied to analyze geospatial data at a global scale.

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Flipboard

NOVEMBER 24, 2023

In an increasingly digital and rapidly changing world, BMW Group’s business and product development strategies rely heavily on data-driven decision-making. With that, the need for data scientists and machine learning (ML) engineers has grown significantly.

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AWS Machine Learning Blog

OCTOBER 20, 2023

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries.

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AWS Machine Learning Blog

OCTOBER 28, 2024

Real-world applications vary in inference requirements for their artificial intelligence and machine learning (AI/ML) solutions to optimize performance and reduce costs. SageMaker Model Monitor monitors the quality of SageMaker ML models in production. Your client applications invoke this endpoint to get inferences from the model.

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AWS Machine Learning Blog

DECEMBER 4, 2024

Businesses are under pressure to show return on investment (ROI) from AI use cases, whether predictive machine learning (ML) or generative AI. Only 54% of ML prototypes make it to production, and only 5% of generative AI use cases make it to production. Using SageMaker, you can build, train and deploy ML models.

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AWS Machine Learning Blog

OCTOBER 29, 2024

Recently, we’ve been witnessing the rapid development and evolution of generative AI applications, with observability and evaluation emerging as critical aspects for developers, data scientists, and stakeholders. This feature allows you to separate data into logical partitions, making it easier to analyze and process data later.

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AWS Machine Learning Blog

MARCH 10, 2025

We walk through the journey Octus took from managing multiple cloud providers and costly GPU instances to implementing a streamlined, cost-effective solution using AWS services including Amazon Bedrock, AWS Fargate , and Amazon OpenSearch Service. Along the way, it also simplified operations as Octus is an AWS shop more generally.

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AWS Machine Learning Blog

MARCH 15, 2023

Tens of thousands of AWS customers use AWS machine learning (ML) services to accelerate their ML development with fully managed infrastructure and tools. We demonstrate how two different personas, a data scientist and an MLOps engineer, can collaborate to lift and shift hundreds of legacy models.

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AWS Machine Learning Blog

JULY 8, 2024

As industries begin adopting processes dependent on machine learning (ML) technologies, it is critical to establish machine learning operations (MLOps) that scale to support growth and utilization of this technology. There were noticeable challenges when running ML workflows in the cloud.

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AWS Machine Learning Blog

JANUARY 5, 2024

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models.

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Data Science Dojo

FEBRUARY 20, 2023

Machine learning (ML) is the technology that automates tasks and provides insights. It allows data scientists to build models that can automate specific tasks. It comes in many forms, with a range of tools and platforms designed to make working with ML more efficient. It also has ML algorithms built into the platform.

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AWS Machine Learning Blog

MARCH 21, 2025

JupyterLab applications flexible and extensive interface can be used to configure and arrange machine learning (ML) workflows. Amazon Simple Storage Service (Amazon S3) Amazon S3 is an object storage service built to store and protect any amount of data. We use JupyterLab to run the code for processing formulae and charts.

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AWS Machine Learning Blog

APRIL 17, 2024

Recent developments in machine learning (ML) have led to increasingly large models, some of which require hundreds of billions of parameters. In such distributed environments, observability of both instances and ML chips becomes key to model performance fine-tuning and cost optimization.

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AWS Machine Learning Blog

FEBRUARY 27, 2025

Analysis The final stage empowers healthcare data scientists with detailed analytical capabilities. Data ScientistGenerative AI, Amazon Bedrock, where he contributes to cutting edge innovations in foundational models and generative AI applications at AWS.

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AWS Machine Learning Blog

AUGUST 5, 2024

This post demonstrates how to seamlessly automate the deployment of an end-to-end RAG solution using Knowledge Bases for Amazon Bedrock and AWS CloudFormation , enabling organizations to quickly and effortlessly set up a powerful RAG system. On the AWS CloudFormation console, create a new stack. txt,md,html,doc/docx,csv,xls/.xlsx,pdf).

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AWS Machine Learning Blog

NOVEMBER 29, 2024

About the Authors Shreyas Subramanian is a Principal Data Scientist and helps customers by using generative AI and deep learning to solve their business challenges using AWS services. Shreyas has a background in large-scale optimization and ML and in the use of ML and reinforcement learning for accelerating optimization tasks.

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AWS Machine Learning Blog

APRIL 26, 2023

Do you need help to move your organization’s Machine Learning (ML) journey from pilot to production? Most executives think ML can apply to any business decision, but on average only half of the ML projects make it to production. Challenges Customers may face several challenges when implementing machine learning (ML) solutions.

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Flipboard

NOVEMBER 30, 2023

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and effortlessly build, train, and deploy machine learning (ML) models at any scale. Deploy traditional models to SageMaker endpoints In the following examples, we showcase how to use ModelBuilder to deploy traditional ML models.

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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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IBM Data Science in Practice

APRIL 4, 2023

Introduction This article shows how to monitor a model deployed on AWS Sagemaker for quality, bias and explainability, using IBM Watson OpenScale on the IBM Cloud Pak for Data platform. This article shows how to use the endpoint generated from that tutorial to demonstrate how to monitor the AWS deployment with Watson OpenScale.

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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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AWS Machine Learning Blog

SEPTEMBER 20, 2023

For example, you might have acquired a company that was already running on a different cloud provider, or you may have a workload that generates value from unique capabilities provided by AWS. We show how you can build and train an ML model in AWS and deploy the model in another platform.

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AWS Machine Learning Blog

MARCH 3, 2025

SageMaker HyperPod recipes help data scientists and developers of all skill sets to get started training and fine-tuning popular publicly available generative AI models in minutes with state-of-the-art training performance. All of this runs under the SageMaker managed environment, providing optimal resource utilization and security.

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AWS Machine Learning Blog

FEBRUARY 13, 2025

AWS Lambda functions for executing specific actions (such as submitting vacation requests or expense reports). A code interpreter tool for performing calculations and data analysis. With a strong background in AI/ML, Ishan specializes in building Generative AI solutions that drive business value. Nitin Eusebius is a Sr.

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AWS Machine Learning Blog

JANUARY 31, 2025

Increasingly, FMs are completing tasks that were previously solved by supervised learning, which is a subset of machine learning (ML) that involves training algorithms using a labeled dataset. With a serverless solution, AWS provides a managed solution, facilitating lower cost of ownership and reduced complexity of maintenance.

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Flipboard

AUGUST 17, 2023

Amazon Redshift is the most popular cloud data warehouse that is used by tens of thousands of customers to analyze exabytes of data every day. SageMaker Studio is the first fully integrated development environment (IDE) for ML. To do this, we provide an AWS CloudFormation template to create a stack that contains the resources.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

Amazon SageMaker is a comprehensive, fully managed machine learning (ML) platform that revolutionizes the entire ML workflow. It offers an unparalleled suite of tools that cater to every stage of the ML lifecycle, from data preparation to model deployment and monitoring. Check out the Cohere on AWS GitHub repo.

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AWS Machine Learning Blog

SEPTEMBER 4, 2024

Its scalability and load-balancing capabilities make it ideal for handling the variable workloads typical of machine learning (ML) applications. Amazon SageMaker provides capabilities to remove the undifferentiated heavy lifting of building and deploying ML models. This entire workflow is shown in the following solution diagram.

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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

NOVEMBER 27, 2024

Launching a machine learning (ML) training cluster with Amazon SageMaker training jobs is a seamless process that begins with a straightforward API call, AWS Command Line Interface (AWS CLI) command, or AWS SDK interaction. Surya Kari is a Senior Generative AI Data Scientist at AWS.

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AWS Machine Learning Blog

DECEMBER 10, 2024

You can try this model with SageMaker JumpStart, a machine learning (ML) hub that provides access to algorithms and models that can be deployed with one click for running inference. This enforces data security and compliance, because the models operate under your own VPC controls, rather than in a shared public environment.

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AWS Machine Learning Blog

JULY 6, 2023

Many organizations choose SageMaker as their ML platform because it provides a common set of tools for developers and data scientists. We also deep dive into the most common architectures and AWS resources to facilitate these integrations.

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