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.

AWS 107

AWS ML ML Data Scientist 107

Analytics Vidhya

MAY 31, 2022

This article was published as a part of the Data Science Blogathon. Introduction on AWS Sagemaker Data scientists need to create, train and deploy a large number of models as they work. AWS has created a simple […].

AWS 268

AWS Data Scientist Data Science Analytics 268

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.

AWS 100

AWS Data Scientist ML ML 100

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/

AWS 109

AWS Clustering Deep Learning Deep Learning 109

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.

AWS 117

AWS ML ML Machine Learning 117

AWS Machine Learning Blog

OCTOBER 16, 2024

The company developed an automated solution called Call Quality (CQ) using AI services from Amazon Web Services (AWS). It uses deep learning to convert audio to text quickly and accurately. AWS Lambda is used in this architecture as a transcription processor to store the processed transcriptions into an Amazon OpenSearch Service table.

AWS 92

AWS AI AI Machine Learning 92

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.

Data Scientist 192

Data Scientist Machine Learning Machine Learning AWS 192

Flipboard

DECEMBER 3, 2024

Syngenta and AWS collaborated to develop Cropwise AI , an innovative solution powered by Amazon Bedrock Agents , to accelerate their sales reps’ ability to place Syngenta seed products with growers across North America. The collaboration between Syngenta and AWS showcases the transformative power of LLMs and AI agents.

AWS 149

AWS AI AI Machine Learning 149

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.

AWS 145

AWS Machine Learning Machine Learning Data Scientist 145

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.

Data Science 84

Data Science AWS Hadoop Data Scientist 84

AWS Machine Learning Blog

MAY 16, 2025

This post is co-authored by Manuel Lopez Roldan, SiMa.ai, and Jason Westra, AWS Senior Solutions Architect. Are you looking to deploy machine learning (ML) models at the edge? Designed to work on SiMas MLSoC (Machine Learning System on Chip) hardware, your models will have seamless compatibility across the entire SiMa.ai

AWS 89

AWS AI AI ML 89

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.

AWS 117

AWS AI AI Data Scientist 117

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.

ML 123

ML ML Clustering Machine Learning 123

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.

AWS 115

AWS Database AI AI 115

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.

AWS 133

AWS Data Scientist AI AI 133

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.

Machine Learning 193

Machine Learning Machine Learning Data Science Data Scientist 193

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.

AWS 65

AWS ML ML Machine Learning 65

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.

Machine Learning 135

Machine Learning Machine Learning AWS Analytics 135

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

AWS 115

AWS Python AI AI 115

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.

AWS 126

AWS ML ML Data Scientist 126

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.

ML 102

ML ML AWS AI 102

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.

Machine Learning 195

Machine Learning Machine Learning AWS Azure 195

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.

Machine Learning 100

Machine Learning Machine Learning AWS Data Science 100

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.

Data Governance 132

Data Governance ML ML Data Lakes 132

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.

Machine Learning 139

Machine Learning Machine Learning ML ML 139

Flipboard

DECEMBER 18, 2023

Customers use Amazon Redshift as a key component of their data architecture to drive use cases from typical dashboarding to self-service analytics, real-time analytics, machine learning (ML), data sharing and monetization, and more.

AWS 139

AWS Data Warehouse ETL SQL 139

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.

ML 119

ML ML AWS AI 119

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.

Machine Learning 126

Machine Learning Machine Learning Data Governance ML 126

AWS Machine Learning Blog

APRIL 30, 2025

We recommend referring to the Submit a model distillation job in Amazon Bedrock in the official AWS documentation for the most up-to-date and comprehensive information. You can track these job status details in both the AWS Management Console and AWS SDK. Prior to joining AWS, he obtained his Ph.D.

AWS 121

AWS AI AI Computer Science 121

AWS Machine Learning Blog

JANUARY 29, 2025

In this post, we explore how to deploy distilled versions of DeepSeek-R1 with Amazon Bedrock Custom Model Import, making them accessible to organizations looking to use state-of-the-art AI capabilities within the secure and scalable AWS infrastructure at an effective cost. You can monitor costs with AWS Cost Explorer.

AWS 135

AWS ML ML AI 135

AWS Machine Learning Blog

SEPTEMBER 18, 2024

In addition to its groundbreaking AI innovations, Zeta Global has harnessed Amazon Elastic Container Service (Amazon ECS) with AWS Fargate to deploy a multitude of smaller models efficiently. Zeta’s AI innovation is powered by a proprietary machine learning operations (MLOps) system, developed in-house.

AWS 126

AWS Machine Learning Machine Learning ML 126

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.

ML 103

ML ML Python AWS 103

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).

AWS 129

AWS Natural Language Processing Machine Learning Machine Learning 129

AWS Machine Learning Blog

NOVEMBER 14, 2024

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts.

AWS 108

AWS ML ML Machine Learning 108

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.

Supervised Learning 103

Supervised Learning AWS Data Scientist ML 103

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.

AWS 136

AWS Data Science ML ML 136

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.

AWS 160

AWS Natural Language Processing Machine Learning Machine Learning 160