AWS Machine Learning Blog

MAY 31, 2023

Running machine learning (ML) workloads with containers is becoming a common practice. What you get is an ML development environment that is consistent and portable. In this post, we show you how to run your ML training jobs in a container using Amazon ECS to deploy, manage, and scale your ML workload.

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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

JANUARY 24, 2024

We demonstrate how to build an end-to-end RAG application using Cohere’s language models through Amazon Bedrock and a Weaviate vector database on AWS Marketplace. Additionally, you can securely integrate and easily deploy your generative AI applications using the AWS tools you are already familiar with.

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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

FEBRUARY 6, 2024

QnABot on AWS is an open source solution built using AWS native services like Amazon Lex , Amazon OpenSearch Service , AWS Lambda , Amazon Transcribe , and Amazon Polly. In this post, we demonstrate how to integrate the QnABot on AWS chatbot solution with ServiceNow. QnABot version 5.4+ Provide a name for the bot.

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AWS Machine Learning Blog

SEPTEMBER 27, 2024

Architecting specific AWS Cloud solutions involves creating diagrams that show relationships and interactions between different services. Instead of building the code manually, you can use Anthropic’s Claude 3’s image analysis capabilities to generate AWS CloudFormation templates by passing an architecture diagram as input.

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Flipboard

NOVEMBER 17, 2023

Amazon SageMaker enables enterprises to build, train, and deploy machine learning (ML) models. Amazon SageMaker JumpStart provides pre-trained models and data to help you get started with ML. This type of data is often used in ML and artificial intelligence applications. As always, AWS welcomes feedback.

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AWS Machine Learning Blog

AUGUST 26, 2024

At AWS, we are transforming our seller and customer journeys by using generative artificial intelligence (AI) across the sales lifecycle. It will be able to answer questions, generate content, and facilitate bidirectional interactions, all while continuously using internal AWS and external data to deliver timely, personalized insights.

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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. However, putting an ML model into production at scale is challenging and requires a set of best practices.

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AWS Machine Learning Blog

JUNE 22, 2023

Working with AWS, Light & Wonder recently developed an industry-first secure solution, Light & Wonder Connect (LnW Connect), to stream telemetry and machine health data from roughly half a million electronic gaming machines distributed across its casino customer base globally when LnW Connect reaches its full potential.

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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

SEPTEMBER 19, 2023

Amazon SageMaker Feature Store provides an end-to-end solution to automate feature engineering for machine learning (ML). For many ML use cases, raw data like log files, sensor readings, or transaction records need to be transformed into meaningful features that are optimized for model training. SageMaker Studio set up.

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AWS Machine Learning Blog

DECEMBER 23, 2024

Lettria , an AWS Partner, demonstrated that integrating graph-based structures into RAG workflows improves answer precision by up to 35% compared to vector-only retrieval methods. In this post, we explore why GraphRAG is more comprehensive and explainable than vector RAG alone, and how you can use this approach using AWS services and Lettria.

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AWS Machine Learning Blog

MAY 4, 2023

This post provides an overview of generative AI with a real customer use case, provides a concise description and outlines its benefits, references an easy-to-follow demo of AWS DeepComposer for creating new musical compositions, and outlines how to get started using Amazon SageMaker JumpStart for deploying GPT2, Stable Diffusion 2.0,

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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. These automations can greatly decrease overhead related to ML project setup, facilitate technical consistency, and save costs related to running idle instances.

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AWS Machine Learning Blog

MAY 31, 2023

PyTorch is a machine learning (ML) framework based on the Torch library, used for applications such as computer vision and natural language processing. This provides a major flexibility advantage over the majority of ML frameworks, which require neural networks to be defined as static objects before runtime.

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AWS Machine Learning Blog

SEPTEMBER 15, 2023

In this post, we introduce LLM agents and demonstrate how to build and deploy an e-commerce LLM agent using Amazon SageMaker JumpStart and AWS Lambda. To power the LLM agent, we use a Flan-UL2 model deployed as a SageMaker endpoint and use data retrieval tools built with AWS Lambda. helper function definitions.

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AWS Machine Learning Blog

OCTOBER 2, 2023

We show you how to use AWS IoT Greengrass to manage model inference at the edge and how to automate the process using AWS Step Functions and other AWS services. AWS IoT Greengrass is an Internet of Things (IoT) open-source edge runtime and cloud service that helps you build, deploy, and manage edge device software.

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O'Reilly Media

OCTOBER 19, 2021

This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. What does a modern technology stack for streamlined ML processes look like?

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AWS Machine Learning Blog

APRIL 16, 2024

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. In the process of working on their ML tasks, data scientists typically start their workflow by discovering relevant data sources and connecting to them. or later image versions.

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AWS Machine Learning Blog

AUGUST 21, 2023

Amazon SageMaker Data Wrangler reduces the time it takes to collect and prepare data for machine learning (ML) from weeks to minutes. Data is frequently kept in data lakes that can be managed by AWS Lake Formation , giving you the ability to implement fine-grained access control using a straightforward grant or revoke procedure.

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AWS Machine Learning Blog

APRIL 25, 2024

The most common techniques used for extractive summarization are term frequency-inverse document frequency (TF-IDF), sentence scoring, text rank algorithm, and supervised machine learning (ML). To learn more about FMEval in AWS and how to use it effectively, refer to Use SageMaker Clarify to evaluate large language models.

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AWS Machine Learning Blog

AUGUST 3, 2023

As machine learning (ML) becomes increasingly prevalent in a wide range of industries, organizations are finding the need to train and serve large numbers of ML models to meet the diverse needs of their customers. Here, the checkpoints need to be saved in a pre-specified location, with the default being /opt/ml/checkpoints.

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AWS Machine Learning Blog

MAY 8, 2023

With Amazon SageMaker , you can manage the whole end-to-end machine learning (ML) lifecycle. It offers many native capabilities to help manage ML workflows aspects, such as experiment tracking, and model governance via the model registry. To automate the infrastructure deployment, we use the AWS Cloud Development Kit (AWS CDK).

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AWS Machine Learning Blog

AUGUST 22, 2024

You can use Amazon SageMaker Model Building Pipelines to collaborate between multiple AI/ML teams. SageMaker Pipelines You can use SageMaker Pipelines to define and orchestrate the various steps involved in the ML lifecycle, such as data preprocessing, model training, evaluation, and deployment.

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AWS Machine Learning Blog

MAY 9, 2023

Amazon SageMaker provides a number of options for users who are looking for a solution to host their machine learning (ML) models. For that use case, SageMaker provides SageMaker single model endpoints (SMEs), which allow you to deploy a single ML model against a logical endpoint. Session()) s3_client = boto3.client("s3")

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AWS Machine Learning Blog

OCTOBER 2, 2023

It is architected to automate the entire machine learning (ML) process, from data labeling to model training and deployment at the edge. On top of that, the whole process can be configured and managed via the AWS SDK, which is what we use to orchestrate our labeling workflow as part of our CI/CD pipeline.

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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. SageMaker is a fully managed service for building, training, and deploying ML models.

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AWS Machine Learning Blog

AUGUST 27, 2024

Currently, users might have to engineer their applications to handle scenarios involving traffic spikes that can use service quotas from multiple regions by implementing complex techniques such as client-side load balancing between AWS regions, where Amazon Bedrock service is supported. Become more resilient to any traffic bursts.

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AWS Machine Learning Blog

AUGUST 20, 2024

So, we introduced a new feature that uses AWS global condition context keys aws:SourceIp and aws:VpcSourceIp to allow customers to restrict presigned URL access to specific IP addresses or VPC endpoints. Before we go into the configuration of this new feature, let’s look at how you can create and update workteams today using the AWS CLI.

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AWS Machine Learning Blog

SEPTEMBER 8, 2023

We’ll cover how technologies such as Amazon Textract, AWS Lambda , Amazon Simple Storage Service (Amazon S3), and Amazon OpenSearch Service can be integrated into a workflow that seamlessly processes documents. The main concepts used are the AWS Cloud Development Kit (CDK) constructs, the actual CDK stacks and AWS Step Functions.

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AWS Machine Learning Blog

MAY 16, 2024

Problem definition Traditionally, the recommendation service was mainly provided by identifying the relationship between products and providing products that were highly relevant to the product selected by the customer. xlarge","Name":"Master Instance Group"},{"InstanceCount":2,"InstanceGroupType":"CORE","InstanceType":"r5.xlarge","Name":"Core

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AWS Machine Learning Blog

OCTOBER 14, 2024

In this post, we show how to create a multimodal chat assistant on Amazon Web Services (AWS) using Amazon Bedrock models, where users can submit images and questions, and text responses will be sourced from a closed set of proprietary documents. For this post, we recommend activating these models in the us-east-1 or us-west-2 AWS Region.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

The IDP Well-Architected Lens is intended for all AWS customers who use AWS to run intelligent document processing (IDP) solutions and are searching for guidance on how to build secure, efficient, and reliable IDP solutions on AWS.

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AWS Machine Learning Blog

JUNE 25, 2024

Although these traditional machine learning (ML) approaches might perform decently in terms of accuracy, there are several significant advantages to adopting generative AI approaches. The following table compares the generative approach (generative AI) with the discriminative approach (traditional ML) across multiple aspects.

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AWS Machine Learning Blog

FEBRUARY 27, 2023

Model tuning is the experimental process of finding the optimal parameters and configurations for a machine learning (ML) model that result in the best possible desired outcome with a validation dataset. Single objective optimization with a performance metric is the most common approach for tuning ML models.

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AWS Machine Learning Blog

OCTOBER 15, 2024

We’re excited to announce the release of SageMaker Core , a new Python SDK from Amazon SageMaker designed to offer an object-oriented approach for managing the machine learning (ML) lifecycle. With SageMaker Core, managing ML workloads on SageMaker becomes simpler and more efficient. and above. Any version above 2.231.0

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