AWS Machine Learning Blog

OCTOBER 15, 2024

In addition to traditional custom-tailored deep learning models, SageMaker Ground Truth also supports generative AI use cases, enabling the generation of high-quality training data for artificial intelligence and machine learning (AI/ML) models. To learn more, see Use Amazon SageMaker Ground Truth Plus to Label Data.

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JULY 11, 2025

With rich experience in Generative AI and diverse areas of ML, Nikita is enthusiastic about using AI to solve challenging real-world business problems across industries. She is passionate about AI/ML, finance and software security topics. Navigate to the Notebook instance and open the IAM role attached tothe notebook.

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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

JULY 23, 2025

Assign the following policy as the trust relationship to your IAM role: { "Version": "2012-10-17", "Statement": [ { "Sid": "", "Effect": "Allow", "Principal": { "Service": [ "bedrock.amazonaws.com", "sagemaker.amazonaws.com" ] }, "Action": "sts:AssumeRole" } ] } Clone the GitHub repository with the assets for this deployment.

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AWS Machine Learning Blog

JANUARY 24, 2025

His core area of focus includes Generative AI and Machine Learning. 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.

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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 10, 2025

With seven years of experience in AI/ML, his expertise spans GenAI and NLP, specializing in designing and deploying agentic AI systems. With a decade of experience at Amazon, having joined in 2012, Kshitiz has gained deep insights into the cloud computing landscape. He specializes in generative AI, machine learning, and system design.

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AWS Machine Learning Blog

JUNE 11, 2025

With a passion for delivering cutting-edge generative AI solutions, Kamran helps customers unlock the full potential of AWS AI/ML services to solve real-world business challenges. Nay Doummar is an Engineering Manager on the Unified Support team at Adobe, where she’s been since 2012.

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AWS Machine Learning Blog

JULY 17, 2025

Today, we’re excited to introduce a comprehensive approach to model evaluation through the Amazon Nova LLM-as-a-Judge capability on Amazon SageMaker AI , a fully managed Amazon Web Services (AWS) service to build, train, and deploy machine learning (ML) models at scale.

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Flipboard

NOVEMBER 15, 2024

The following is an example inline policy: { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": [ "s3:GetObject", "s3:PutObject" ], "Resource": [ "arn:aws:s3:::aws-gen-ai-glue-metadata-*/*" ] } ] } An IAM role for your notebook environment. Anastasia Tzeveleka is a Senior GenAI/ML Specialist Solutions Architect at AWS.

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Flipboard

JULY 25, 2025

A study from researchers at the University of California, Riverside , for instance, estimated the average US AI data center used for OpenAIs GPT-3 consumed nearly 17 ml of water per LLM prompt. A call for more data Mistrals numbers are broadly comparable to other studies that have sought to estimate AIs environmental impact.

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AWS Machine Learning Blog

MARCH 27, 2025

As AI and machine learning capabilities continue to evolve, finding the right balance between security controls and innovation enablement will remain a key challenge for organizations. Dhawal Patel is a Principal Machine Learning Architect at AWS. Lets name this IAM role Bedrock-Access-CRI.

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Flipboard

APRIL 14, 2025

More specifically, it's the AI and machine-learning group that's getting the lion's share of mockery. Known as AI/ML for short, its woes only deepened after Apple announced that it had to delay its much-hyped next iteration of AI enhancements for Siri until 2026. The moniker is also a jab at AI/ML's ousted leaders.

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JULY 17, 2025

Ashish Lal is an AI/ML Senior Product Marketing Manager for Amazon Bedrock. If you’re using a customer managed key for your S3 Vector as a vector store, you’ll need to provide an additional policy to allow the decryption of the data. In her spare time, she loves reading and cooking for her friends.

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AWS Machine Learning Blog

MARCH 19, 2025

nn en nn nnAWS (Amazon Web Services) is a cloud computing platform that offers a broad set of global services including computing, storage, databases, analytics, machine learning, and more. He helps enterprise customers to achieve business outcomes by unlocking the full potential of AI/ML services on the AWS Cloud.

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AWS Machine Learning Blog

FEBRUARY 12, 2025

About the Author Sujeong Cha is a Deep Learning Architect at the AWS Generative AI Innovation Center, where she specializes in model customization and optimization. She has extensive hands-on experience in solving customers business use cases by utilizing generative AI as well as traditional AI/ML solutions. Sujeong holds a M.S.

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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. Set up a Boto3 client for SageMaker: sm_client = boto3.client('sagemaker')

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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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Hacker News

JUNE 29, 2025

reply ml- 1 day ago | prev | next [–] Still on my sabbatical and continuing to build on things I enjoy rather than things that pay (for now). reply ml- 18 hours ago | root | parent | next [–] Appreciate it! ML runs locally, no clouds, uploads etc. It was incredibly useful to me, thanks! Really cool idea.

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IBM Data Science in Practice

MARCH 8, 2023

Feature Platforms — A New Paradigm in Machine Learning Operations (MLOps) Operationalizing Machine Learning is Still Hard OpenAI introduced ChatGPT. The growth of the AI and Machine Learning (ML) industry has continued to grow at a rapid rate over recent years.

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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.

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Machine Learning (Theory)

JULY 19, 2021

A general theme of the invited talks this year is “ machine learning for science.” The Program Chairs (Marina Meila and Tong Zhang) have invited world-renowned scientists from various disciplines to discuss their problems and the corresponding machine learning challenges.

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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

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.

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Flipboard

AUGUST 17, 2023

Many practitioners are extending these Redshift datasets at scale for machine learning (ML) using Amazon SageMaker , a fully managed ML service, with requirements to develop features offline in a code way or low-code/no-code way, store featured data from Amazon Redshift, and make this happen at scale in a production environment.

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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). You can preview the prediction, download it to a local machine, delete it, or send it to QuickSight.

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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

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.

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AWS Machine Learning Blog

DECEMBER 29, 2022

revolution has shown the value and importance of machine learning (ML) across verticals and environments, with more impact on manufacturing than possibly any other application. These are the real-time datasets that will be used for inferencing with the ML model. The last decade of the Industry 4.0 Choose Add.

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AWS Machine Learning Blog

JULY 10, 2025

AI developers and machine learning (ML) engineers can now use the capabilities of Amazon SageMaker Studio directly from their local Visual Studio Code (VS Code). The solution architecture consists of three main components: Local computer : Your development machine running VS Code with AWS Toolkit extension installed.

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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

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. With the recent PyTorch 2.0 release, AWS customers can now do same things as they could with PyTorch 1.x Refer to PyTorch 2.0:

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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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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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Iguazio

AUGUST 3, 2023

How can retailers use, grow and optimize their use of data and machine learning? For data scientists tasked with building and training machine learning models for retailers, open and free retail datasets are an important starting point. To learn more about ML and retailers, click here. Get the dataset here.

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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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