AWS Machine Learning Blog

DECEMBER 11, 2024

Earlier this year, we published the first in a series of posts about how AWS is transforming our seller and customer journeys using generative AI. Field Advisor serves four primary use cases: AWS-specific knowledge search With Amazon Q Business, weve made internal data sources as well as public AWS content available in Field Advisors index.

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Data Science Dojo

OCTOBER 31, 2024

Key Skills: Mastery in machine learning frameworks like PyTorch or TensorFlow is essential, along with a solid foundation in unsupervised learning methods. Applied Machine Learning Scientist Description : Applied ML Scientists focus on translating algorithms into scalable, real-world applications.

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insideBIGDATA

JUNE 22, 2023

AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), today announced the AWS Generative AI Innovation Center, a new program to help customers successfully build and deploy generative artificial intelligence (AI) solutions. Amazon Web Services, Inc.

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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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APRIL 24, 2025

These tables house complex domain-specific schemas, with instances of nested tables and multi-dimensional data that require complex database queries and domain-specific knowledge for data retrieval. As a result, NL2SQL solutions for enterprise data are often incomplete or inaccurate.

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AWS Machine Learning Blog

JANUARY 7, 2025

Translation memory A translation memory is a database that stores previously translated text segments (typically sentences or phrases) along with their corresponding translations. The main purpose of a TM is to aid human or machine translators by providing them with suggestions for segments that have already been translated before.

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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.

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Flipboard

DECEMBER 1, 2024

AWS DMS Schema Conversion converts up to 90% of your schema to accelerate your database migrations and reduce manual effort with the power of generative AI.

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Flipboard

NOVEMBER 27, 2024

While customers can perform some basic analysis within their operational or transactional databases, many still need to build custom data pipelines that use batch or streaming jobs to extract, transform, and load (ETL) data into their data warehouse for more comprehensive analysis. or a later version) database.

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AWS Machine Learning Blog

DECEMBER 16, 2024

We spoke with Dr. Swami Sivasubramanian, Vice President of Data and AI, shortly after AWS re:Invent 2024 to hear his impressionsand to get insights on how the latest AWS innovations help meet the real-world needs of customers as they build and scale transformative generative AI applications. Canva uses AWS to power 1.2

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AWS Machine Learning Blog

NOVEMBER 22, 2024

This post discusses how to use AWS Step Functions to efficiently coordinate multi-step generative AI workflows, such as parallelizing API calls to Amazon Bedrock to quickly gather answers to lists of submitted questions. sync) pattern, which automatically waits for the completion of asynchronous jobs.

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Flipboard

APRIL 24, 2025

OpenSearch Service is the AWS recommended vector database for Amazon Bedrock. Its a fully managed service that you can use to deploy, operate, and scale OpenSearch on AWS. OpenSearch is a distributed open-source search and analytics engine composed of a search engine and vector database. An OpenSearch Service domain.

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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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Flipboard

APRIL 21, 2025

Evaluation plays a central role in the generative AI application lifecycle, much like in traditional machine learning. In this post, to address the aforementioned challenges, we introduce an automated evaluation framework that is deployable on AWS. Additionally, we provide a user-friendly interface to enhance ease of use.

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Flipboard

DECEMBER 3, 2024

AWS Database Migration Service Schema Conversion (DMS SC) helps you accelerate your database migration to AWS. Using DMS SC, you can assess, convert, …

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AWS Machine Learning Blog

MARCH 10, 2023

This approach allows you to react to the potentially fraudulent transactions in real time as you store each transaction in a database and inspect it before processing further. You can use this metadata in your data analytics solutions, machine learning model training tasks, or visualizations and dashboards that consume transaction data.

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AWS Machine Learning Blog

NOVEMBER 20, 2024

Whether it’s structured data in databases or unstructured content in document repositories, enterprises often struggle to efficiently query and use this wealth of information. The solution combines data from an Amazon Aurora MySQL-Compatible Edition database and data stored in an Amazon Simple Storage Service (Amazon S3) bucket.

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AWS Machine Learning Blog

OCTOBER 31, 2024

AWS offers powerful generative AI services , including Amazon Bedrock , which allows organizations to create tailored use cases such as AI chat-based assistants that give answers based on knowledge contained in the customers’ documents, and much more. The following figure illustrates the high-level design of the solution.

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AWS Machine Learning Blog

APRIL 7, 2025

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies and AWS. Solution overview The following diagram provides a high-level overview of AWS services and features through a sample use case.

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AWS Machine Learning Blog

JUNE 11, 2024

In this post, we describe the scale of our AI offerings, the challenges with diverse AI workloads, and how we optimized mixed AI workload inference performance with AWS Graviton3 based c7g instances and achieved 20% throughput improvement, 30% latency reduction, and reduced our cost by 25–30%.

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Flipboard

APRIL 23, 2025

To assist in this effort, AWS provides a range of generative AI security strategies that you can use to create appropriate threat models. For all data stored in Amazon Bedrock, the AWS shared responsibility model applies. The following diagram illustrates how RBAC works with metadata filtering in the vector database.

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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

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. The user query is used to retrieve relevant additional context from the vector database. The user receives a more accurate response based on their query.

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AWS Machine Learning Blog

OCTOBER 29, 2024

This solution uses decorators in your application code to capture and log metadata such as input prompts, output results, run time, and custom metadata, offering enhanced security, ease of use, flexibility, and integration with native AWS services.

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AWS Machine Learning Blog

NOVEMBER 7, 2024

The Lambda function runs the database query against the appropriate OpenSearch Service indexes, searching for exact matches or using fuzzy matching for partial information. For specific part inquiries, the agent consults the action groups available to the agent and invokes the correct action (API) to retrieve relevant information.

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AWS Machine Learning Blog

APRIL 3, 2025

Thats why we at Amazon Web Services (AWS) are working on AI Workforcea system that uses drones and AI to make these inspections safer, faster, and more accurate. This post is the first in a three-part series exploring AI Workforce, the AWS AI-powered drone inspection system. In this post, we introduce the concept and key benefits.

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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.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

In this post, we save the data in JSON format, but you can also choose to store it in your preferred SQL or NoSQL database. Prerequisites To perform this solution, complete the following: Create and activate an AWS account. Make sure your AWS credentials are configured correctly. or later on your local machine.

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AWS Machine Learning Blog

OCTOBER 29, 2024

This engine uses artificial intelligence (AI) and machine learning (ML) services and generative AI on AWS to extract transcripts, produce a summary, and provide a sentiment for the call. Organizations typically can’t predict their call patterns, so the solution relies on AWS serverless services to scale during busy times.

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Flipboard

NOVEMBER 7, 2023

“Data is at the center of every application, process, and business decision,” wrote Swami Sivasubramanian, VP of Database, Analytics, and Machine Learning at AWS, and I couldn’t agree more. A common pattern customers use today is to build data pipelines to move data from Amazon Aurora to Amazon Redshift.

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insideBIGDATA

OCTOBER 6, 2023

Amazon Web Services (AWS) announced the general availability of Amazon DataZone, a data management service that enables customers to catalog, discover, govern, share, and analyze data at scale across organizational boundaries.

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Flipboard

NOVEMBER 19, 2024

In semantic search, documents are stored as vectors, a numeric representation of the document content, in a vector database such as Amazon OpenSearch Service , and are retrieved by performing similarity search with a vector representation of the search query. If you don’t already have an AWS account, you can create one.

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Flipboard

OCTOBER 25, 2023

However, with the help of AI and machine learning (ML), new software tools are now available to unearth the value of unstructured data. In this post, we discuss how AWS can help you successfully address the challenges of extracting insights from unstructured data. Let’s understand how these AWS services are integrated in detail.

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AWS Machine Learning Blog

JANUARY 30, 2025

The AWS Social Responsibility & Impact (SRI) team recognized an opportunity to augment this function using generative AI. Historically, AWS Health Equity Initiative applications were reviewed manually by a review committee. The team used DynamoDB, a NoSQL database, to store the personas, rubrics, and submitted proposals.

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AWS Machine Learning Blog

DECEMBER 15, 2023

We are excited to announce the launch of Amazon DocumentDB (with MongoDB compatibility) integration with Amazon SageMaker Canvas , allowing Amazon DocumentDB customers to build and use generative AI and machine learning (ML) solutions without writing code. Prepare data for machine learning. Choose Add connection.

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AWS Machine Learning Blog

MARCH 17, 2025

Furthermore, healthcare decisions often require integrating information from multiple sources, such as medical literature, clinical databases, and patient records. AWS Lambda orchestrator, along with tool configuration and prompts, handles orchestration and invokes the Mistral model on Amazon Bedrock.

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AWS Machine Learning Blog

OCTOBER 23, 2024

Agent Creator is a versatile extension to the SnapLogic platform that is compatible with modern databases, APIs, and even legacy mainframe systems, fostering seamless integration across various data environments. Pre-built templates tailored to various use cases are included, significantly enhancing both employee and customer experiences.

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