AWS Machine Learning Blog

SEPTEMBER 23, 2024

Launched in 2021, Amazon SageMaker Canvas is a visual point-and-click service that allows business analysts and citizen data scientists to use ready-to-use machine learning (ML) models and build custom ML models to generate accurate predictions without writing any code. This way, users can only invoke the allowed models.

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AWS Machine Learning Blog

MAY 10, 2023

Quick iteration and faster time-to-value can be achieved by providing these analysts with a visual business intelligence (BI) tool for simple analysis, supported by technologies like machine learning (ML). Through this capability, ML becomes more accessible to business teams so they can accelerate data-driven decision-making.

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AWS Machine Learning Blog

JANUARY 24, 2025

His focus area is AI/ML and Energy & Utilities Segment. Lets create an Amazon S3 gateway endpoint and attach it to VPC with custom IAM resource-based policies to more tightly control access to your Amazon S3 files. The following code is a sample resource policy. Provide your account, bucket name, and VPC settings. Choose Create roles.

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AWS Machine Learning Blog

OCTOBER 30, 2023

On the JSON tab, modify the policy as follows: { "Version": "2012-10-17", "Statement": [ { "Sid": "eniperms", "Effect": "Allow", "Action": [ "ec2:CreateNetworkInterface", "ec2:DescribeNetworkInterfaces", "ec2:DeleteNetworkInterface", "ec2:*VpcEndpoint*" ], "Resource": "*" } ] } Choose Next. You’re redirected to the IAM console. With an M.Sc.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

jpg", "prompt": "Which part of Virginia is this letter sent from", "completion": "Richmond"} SageMaker JumpStart SageMaker JumpStart is a powerful feature within the SageMaker machine learning (ML) environment that provides ML practitioners a comprehensive hub of publicly available and proprietary foundation models (FMs).

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AWS Machine Learning Blog

MARCH 26, 2025

Amazon SageMaker JumpStart is a machine learning (ML) hub that provides pre-trained models, solution templates, and algorithms to help developers quickly get started with machine learning. Today, we are announcing an enhanced private hub feature with several new capabilities that give organizations greater control over their ML assets.

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phData

MAY 17, 2024

The brand-new Forecasting tool created on Snowflake Data Cloud Cortex ML allows you to do just that. What is Cortex ML, and Why Does it Matter? Cortex ML is Snowflake’s newest feature, added to enhance the ease of use and low-code functionality of your business’s machine learning needs.

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Flipboard

APRIL 24, 2025

Create a connector for Amazon Bedrock in OpenSearch Service To use OpenSearch Service machine learning (ML) connectors with other AWS services, you need to set up an IAM role allowing access to that service. Familiarity with Python programming language. The code is open source and hosted on GitHub.

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AWS Machine Learning Blog

JUNE 6, 2023

PyTorch is a machine learning (ML) framework that is widely used by AWS customers for a variety of applications, such as computer vision, natural language processing, content creation, and more. Instead, you can focus on the higher value-added effort of training jobs at scale in a shorter amount of time and iterating on your ML models faster.

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Towards AI

MARCH 14, 2024

All the way back in 2012, Harvard Business Review said that Data Science was the sexiest job of the 21st century and recently followed up with an updated version of their article. I mean, ML engineers often spend most of their time handling and understanding data. So, how is a data scientist different from an ML engineer?

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AWS Machine Learning Blog

SEPTEMBER 3, 2024

This allows SageMaker Studio users to perform petabyte-scale interactive data preparation, exploration, and machine learning (ML) directly within their familiar Studio notebooks, without the need to manage the underlying compute infrastructure. This same interface is also used for provisioning EMR clusters. elasticmapreduce", "arn:aws:s3:::*.elasticmapreduce/*"

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AWS Machine Learning Blog

NOVEMBER 9, 2023

Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance.

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AWS Machine Learning Blog

AUGUST 4, 2023

This completes the setup to enable data access from Salesforce Data Cloud to SageMaker Studio to build AI and machine learning (ML) models. In this step, we use some of these transformations to prepare the dataset for an ML model. Rachna Chadha is a Principal Solutions Architect AI/ML in Strategic Accounts at AWS.

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Flipboard

NOVEMBER 6, 2024

The model is trained on abdominal scans from Far Eastern Memorial Hospital (January 2012–December 2021) and evaluated using a simulated test set (14,039 scans) and a prospective test set (6351 scans) collected from the same center between December 2022 and May 2023. Overall, the model achieves a sensitivity of 0.81–0.83

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AWS Machine Learning Blog

AUGUST 31, 2023

As Artificial Intelligence (AI) and Machine Learning (ML) technologies have become mainstream, many enterprises have been successful in building critical business applications powered by ML models at scale in production.

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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. The SageMaker Processing job operates with the /opt/ml local path, and you can specify your ProcessingInputs and their local path in the configuration. Create an S3 bucket.

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AWS Machine Learning Blog

DECEMBER 4, 2024

SageMaker JumpStart helps you get started with machine learning (ML) by providing fully customizable solutions and one-click deployment and fine-tuning of more than 400 popular open-weight and proprietary generative AI models. Before this role, he obtained an MS in Computer Science from NYU Tandon School of Engineering.

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AWS Machine Learning Blog

MARCH 13, 2024

Advancements in artificial intelligence (AI) and machine learning (ML) are revolutionizing the financial industry for use cases such as fraud detection, credit worthiness assessment, and trading strategy optimization. Don’t change or edit any Block Public Access settings for this access point (all public access should be blocked).

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AWS Machine Learning Blog

SEPTEMBER 27, 2024

Ref S3BucketName, '/*']] - PolicyName: SNSPublish PolicyDocument: Version: '2012-10-17' Statement: - Effect: Allow Action: - 'sns:Publish' Resource: !Ref Ref S3BucketName, '/*']] - PolicyName: SNSPublish PolicyDocument: Version: '2012-10-17' Statement: - Effect: Allow Action: - 'sns:Publish' Resource: !Ref Join ['', ['arn:aws:s3:::',

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AWS Machine Learning Blog

SEPTEMBER 7, 2023

Amazon SageMaker Pipelines is a fully managed AWS service for building and orchestrating machine learning (ML) workflows. SageMaker Pipelines offers ML application developers the ability to orchestrate different steps of the ML workflow, including data loading, data transformation, training, tuning, and deployment.

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AWS Machine Learning Blog

MAY 1, 2025

About the authors Yanyan Zhang is a Senior Generative AI Data Scientist at Amazon Web Services, where she has been working on cutting-edge AI/ML technologies as a Generative AI Specialist, helping customers use generative AI to achieve their desired outcomes. Sovik Kumar Nath is an AI/ML and Generative AI senior solution architect with AWS.

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AWS Machine Learning Blog

MARCH 27, 2025

He has over 12 years of product management experience across a variety of domains and is passionate about AI/ML. The following code is an example of how to modify an existing SCP that denies access to all services in specific Regions while allowing Amazon Bedrock inference through cross-Region inference for Anthropics Claude 3.5

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AWS Machine Learning Blog

DECEMBER 14, 2023

Amazon SageMaker Studio offers a broad set of fully managed integrated development environments (IDEs) for machine learning (ML) development, including JupyterLab, Code Editor based on Code-OSS (Visual Studio Code Open Source), and RStudio. It’s attached to a ML compute instance whenever a Space is run.

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AWS Machine Learning Blog

MAY 21, 2025

About the Authors Mohammad Tahsin is an AI/ML Specialist Solutions Architect at AWS. He lives for staying up-to-date with the latest technologies in AI/ML and helping customers deploy bespoke solutions on AWS. Felipe Lopez is a Senior AI/ML Specialist Solutions Architect at AWS.

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AWS Machine Learning Blog

JUNE 21, 2024

Amazon SageMaker JumpStart is a machine learning (ML) hub offering pre-trained models and pre-built solutions. The private hub uses the SageMaker underlying infrastructure, allowing it to scale with enterprise-level ML demands. He works with government-sponsored entities, helping them build AI/ML solutions using AWS.

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AWS Machine Learning Blog

AUGUST 22, 2023

Machine learning (ML) is revolutionizing solutions across industries and driving new forms of insights and intelligence from data. Many ML algorithms train over large datasets, generalizing patterns it finds in the data and inferring results from those patterns as new unseen records are processed. What is federated learning?

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AWS Machine Learning Blog

OCTOBER 17, 2024

Amazon SageMaker Studio is the latest web-based experience for running end-to-end machine learning (ML) workflows. This can be useful for organizations that want to provide a centralized storage solution for their ML projects across multiple SageMaker Studio domains. In her free time, Irene enjoys traveling and hiking.

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AWS Machine Learning Blog

JUNE 15, 2023

With a background in AI/ML, data science, and analytics, Yunfei helps customers adopt AWS services to deliver business results. He designs AI/ML and data analytics solutions that overcome complex technical challenges and drive strategic objectives. About the authors Yunfei Bai is a Senior Solutions Architect at AWS.

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AWS Machine Learning Blog

APRIL 5, 2023

Amazon Rekognition makes it easy to add this capability to your applications without any machine learning (ML) expertise and comes with various APIs to fulfil use cases such as object detection, content moderation, face detection and analysis, and text and celebrity recognition, which we use in this example.

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Flipboard

MARCH 7, 2023

Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. A public GitHub repo provides hands-on examples for each of the presented approaches.

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The MLOps Blog

NOVEMBER 30, 2022

In this article, you will learn about: the challenges plaguing the ML space and why conventional tools are not the right answer to them. ML model versioning: where are we at? Starting from AlexNet with 8 layers in 2012 to ResNet with 152 layers in 2015 – the deep neural networks have become deeper with time.

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AWS Machine Learning Blog

JULY 17, 2023

With cloud computing, as compute power and data became more available, machine learning (ML) is now making an impact across every industry and is a core part of every business and industry. Amazon SageMaker Studio is the first fully integrated ML development environment (IDE) with a web-based visual interface.

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AWS Machine Learning Blog

MAY 12, 2023

Amazon SageMaker comes with two options to spin up fully managed notebooks for exploring data and building machine learning (ML) models. In addition to creating notebooks, you can perform all the ML development steps to build, train, debug, track, deploy, and monitor your models in a single pane of glass in Studio.

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AWS Machine Learning Blog

JUNE 6, 2025

Below is an example of a trust policy. { "Version": "2012-10-17", "Statement": [ { "Sid": "AllowEksAuthToAssumeRoleForPodIdentity", "Effect": "Allow", "Principal": { "Service": "pods.eks.amazonaws.com" }, "Action": [ "sts:AssumeRole", "sts:TagSession" ] } ] } In Account C, create an S3 access point by following the steps here.

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AWS Machine Learning Blog

NOVEMBER 13, 2024

He is particularly passionate about AI/ML and enjoys building proof-of-concept solutions for his customers. For more information on managing credentials securely, see the AWS Boto3 documentation. Based in Toronto, he focuses on empowering his customers to expand their understanding of AWS and utilize the cloud for innovative use cases.

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DataRobot

NOVEMBER 12, 2021

As ML technologists, we must ensure that technology is built in a way that supports a diverse and equitable implementation rather than reinforcing historical mistakes or amplifying bias. AI Implementers: The IT organization that must inherit a model, whether ML Engineers, or more generally ML Ops personnel.

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AWS Machine Learning Blog

MAY 10, 2023

Jupyter notebooks are highly favored by data scientists for their ability to interactively process data, build ML models, and test these models by making inferences on data. Durga Sury is an ML Solutions Architect on the Amazon SageMaker Service SA team. She is passionate about making machine learning accessible to everyone.

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