AWS Machine Learning Blog

DECEMBER 9, 2024

With access to a wide range of generative AI foundation models (FM) and the ability to build and train their own machine learning (ML) models in Amazon SageMaker , users want a seamless and secure way to experiment with and select the models that deliver the most value for their business.

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KDnuggets

NOVEMBER 10, 2021

Data Scientist Career Path: from Novice to First Job; Understand Neural Networks from a Bayesian Perspective; The Best Ways for Data Professionals to Market AWS Skills; Build Your Own Automated Machine Learning App.

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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

OCTOBER 22, 2024

Amazon SageMaker is a cloud-based machine learning (ML) platform within the AWS ecosystem that offers developers a seamless and convenient way to build, train, and deploy ML models. By using a combination of AWS services, you can implement this feature effectively, overcoming the current limitations within SageMaker.

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AWS Machine Learning Blog

NOVEMBER 26, 2024

Amazon SageMaker Studio is the first integrated development environment (IDE) purposefully designed to accelerate end-to-end machine learning (ML) development. You can create multiple Amazon SageMaker domains , which define environments with dedicated data storage, security policies, and networking configurations.

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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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Dataconomy

APRIL 28, 2023

Machine learning deployment is a crucial step in bringing the benefits of data science to real-world applications. With the increasing demand for machine learning deployment, various tools and platforms have emerged to help data scientists and developers deploy their models quickly and efficiently.

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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

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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Flipboard

JANUARY 6, 2025

(Precise), an Amazon Web Services (AWS) Partner , participated in the AWS Think Big for Small Business Program (TBSB) to expand their AWS capabilities and to grow their business in the public sector. This customer wanted to use machine learning as a tool to digitize images and recognize handwriting.

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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. See Amazon SageMaker geospatial capabilities to learn more. About the Author Xiong Zhou is a Senior Applied Scientist at AWS.

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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

DECEMBER 19, 2023

Artificial intelligence (AI) and machine learning (ML) are becoming an integral part of systems and processes, enabling decisions in real time, thereby driving top and bottom-line improvements across organizations. Machine learning operations (MLOps) applies DevOps principles to ML systems.

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Data Science Dojo

FEBRUARY 20, 2023

These tools will help you streamline your machine learning workflow, reduce operational overheads, and improve team collaboration and communication. Machine learning (ML) is the technology that automates tasks and provides insights. It allows data scientists to build models that can automate specific tasks.

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Flipboard

NOVEMBER 22, 2024

This post is part of an ongoing series about governing the machine learning (ML) lifecycle at scale. This post dives deep into how to set up data governance at scale using Amazon DataZone for the data mesh. To view this series from the beginning, start with Part 1.

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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

NOVEMBER 14, 2024

Streamlit is an open source framework for data scientists to efficiently create interactive web-based data applications in pure Python. Prerequisites To perform this solution, complete the following: Create and activate an AWS account. Make sure your AWS credentials are configured correctly. Install Python 3.7

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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

OCTOBER 1, 2024

Over the last 18 months, AWS has announced more than twice as many machine learning (ML) and generative artificial intelligence (AI) features into general availability than the other major cloud providers combined. The following figure highlights where AWS lands in the DSML Magic Quadrant.

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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. AWS CloudTrail – Monitors and records account activity across AWS infrastructure.

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AWS Machine Learning Blog

FEBRUARY 27, 2024

In this post, we share how Axfood, a large Swedish food retailer, improved operations and scalability of their existing artificial intelligence (AI) and machine learning (ML) operations by prototyping in close collaboration with AWS experts and using Amazon SageMaker.

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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. Generate accurate training data for SageMaker models – For model training, data scientists can use Tecton’s SDK within their SageMaker notebooks to retrieve historical features.

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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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Flipboard

NOVEMBER 20, 2024

Prerequisites Before proceeding with this tutorial, make sure you have the following in place: AWS account – You should have an AWS account with access to Amazon Bedrock. She leads machine learning projects in various domains such as computer vision, natural language processing, and generative AI. model in Amazon Bedrock.

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AWS Machine Learning Blog

AUGUST 21, 2024

Amazon DataZone is a data management service that makes it quick and convenient to catalog, discover, share, and govern data stored in AWS, on-premises, and third-party sources. Solution overview In this section, we provide an overview of three personas: the data admin, data publisher, and data scientist.

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Data Science Dojo

AUGUST 24, 2023

While DevOps and MLOps share many similarities, MLOps requires a more specialized set of tools and practices to address the unique challenges posed by data-driven and computationally intensive ML workflows. AWS Integration : As Netflix developed Metaflow, it closely integrates with Amazon Web Services (AWS) infrastructure.

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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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IBM Data Science in Practice

MARCH 8, 2023

Introduction This article shows how to build a machine learning model on a Watson Studio platform running on IBM Cloud Pak for Data (CPD). You can then export the model and deploy it on Amazon Sagemaker on Amazon Web Server (AWS). environment running on IBM Cloud Pak for Data 4.5.x,

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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. aws s3 rm s3://{bucket}/{prefix_name}/model-monitor/data-capture/{predictor.endpoint_name} --recursive ! Data Scientist with AWS Professional Services.

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Towards AI

JANUARY 29, 2025

Generative AI is powered by advanced machine learning techniques, particularly deep learning and neural networks, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Roles like AI Engineer, Machine Learning Engineer, and Data Scientist are increasingly requiring expertise in Generative AI.

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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

APRIL 17, 2024

Recent developments in machine learning (ML) have led to increasingly large models, some of which require hundreds of billions of parameters. For data scientists, ML chips utilization and saturation are also relevant for capacity planning. Solution overview The following diagram illustrates the solution architecture.

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AWS Machine Learning Blog

DECEMBER 12, 2024

The higher-level abstracted layer is designed for data scientists with limited AWS expertise, offering a simplified interface that hides complex infrastructure details. Data scientists can also seamlessly transition from local training to remote training and training on multiple nodes using the ModelTrainer.

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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. About the Authors Adewale Akinfaderin is a Sr.

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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

SEPTEMBER 18, 2023

Machine learning (ML) is becoming increasingly complex as customers try to solve more and more challenging problems. This complexity often leads to the need for distributed ML, where multiple machines are used to train a single model. The full code can be found on the aws-samples-for-ray GitHub repository.

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