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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databricks

JUNE 27, 2023

We are thrilled to announce that materialized views and streaming tables are now publicly available in Databricks SQL on AWS and Azure. Streaming.

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databricks

OCTOBER 13, 2023

Today, we are excited to announce the general availability of the Databricks SQL Statement Execution API on AWS and Azure, with support for.

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AWS Machine Learning Blog

NOVEMBER 7, 2024

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. You can use AWS services such as Application Load Balancer to implement this approach. On AWS, you can use the fully managed Amazon Bedrock Agents or tools of your choice such as LangChain agents or LlamaIndex agents.

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AWS Machine Learning Blog

JANUARY 15, 2025

Powered by generative AI services on AWS and large language models (LLMs) multi-modal capabilities, HCLTechs AutoWise Companion provides a seamless and impactful experience. This seamless process is facilitated by Retrieval Augmentation Generation (RAG) and a text-to-SQL framework.

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AWS Machine Learning Blog

APRIL 3, 2025

This post explores how OMRON Europe is using Amazon Web Services (AWS) to build its advanced ODAP and its progress toward harnessing the power of generative AI. Some of these tools included AWS Cloud based solutions, such as AWS Lambda and AWS Step Functions.

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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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Flipboard

MAY 28, 2025

Nine out of ten biopharma companies are AWS customers, and helping them streamline and transform the M2M processes can help deliver drugs to patients faster, reduce risk, and bring value to our customers. Finally, we present instructions to deploy the service in your own AWS account.

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Flipboard

NOVEMBER 27, 2024

Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing ETL, business intelligence (BI), and reporting tools. Choose the us-east-1 AWS Region in which to create the stack. Create dbt models in dbt Cloud.

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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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AWS Machine Learning Blog

NOVEMBER 12, 2024

In this blog post, we demonstrate prompt engineering techniques to generate accurate and relevant analysis of tabular data using industry-specific language. You may check out additional reference notebooks on aws-samples for how to use Meta’s Llama models hosted on Amazon Bedrock.

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AWS Machine Learning Blog

FEBRUARY 10, 2025

Portfolio agent: Text-to-SQL and self-correction To boost the productivity of credit portfolio teams, we focused on two key areas. This led us to base our solution on a text-to-SQL model to efficiently bridge the gap between natural language and SQL. Its also adept at troubleshooting coding errors.

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AWS Machine Learning Blog

FEBRUARY 21, 2025

Communication between the two systems was established through Kerberized Apache Livy (HTTPS) connections over AWS PrivateLink. Apache Hive was used to provide a tabular interface to data stored in HDFS, and to integrate with Apache Spark SQL. Rockets new data science solution architecture on AWS is shown in the following diagram.

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AWS Machine Learning Blog

APRIL 14, 2025

Text-to-SQL empowers people to explore data and draw insights using natural language, without requiring specialized database knowledge. Amazon Web Services (AWS) has helped many customers connect this text-to-SQL capability with their own data, which means more employees can generate insights.

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databricks

APRIL 17, 2024

The next generation of Databricks SQL dashboards, also known as Lakeview Dashboards, is now generally available on AWS and Azure. This new dashboarding experience is optimized for ease of use, scalable and secure distribution, governance, and performance.

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AWS Machine Learning Blog

OCTOBER 24, 2024

These agents work with AWS managed infrastructure capabilities and Amazon Bedrock , reducing infrastructure management overhead. The agent can recommend software and architecture design best practices using the AWS Well-Architected Framework for the overall system design. What are the top five most expensive products?

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AWS Machine Learning Blog

DECEMBER 4, 2024

SageMaker Unified Studio combines various AWS services, including Amazon Bedrock , Amazon SageMaker , Amazon Redshift , Amazon Glue , Amazon Athena , and Amazon Managed Workflows for Apache Airflow (MWAA) , into a comprehensive data and AI development platform. Navigate to the AWS Secrets Manager console and find the secret -api-keys.

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databricks

AUGUST 2, 2023

We're excited to share a new set of security controls and compliance certifications that can help with regulatory compliance on Azure Databricks and.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

For a multi-account environment, you can track costs at an AWS account level to associate expenses. A combination of an AWS account and tags provides the best results. Implement a tagging strategy A tag is a label you assign to an AWS resource. The AWS reserved prefix aws: tags provide additional metadata tracked by AWS.

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AWS Machine Learning Blog

NOVEMBER 26, 2024

Now, consider a different scenario: an AI assistant, designed to assist back desk agents at this travel company, uses an LLM to translate natural language queries into SQL commands. We need every user request mapped accurately to its corresponding SQL command, leaving no room for error. Precision is key here.

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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. Install Python 3.7

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AWS Machine Learning Blog

APRIL 16, 2024

They then use SQL to explore, analyze, visualize, and integrate data from various sources before using it in their ML training and inference. Previously, data scientists often found themselves juggling multiple tools to support SQL in their workflow, which hindered productivity.

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AWS Machine Learning Blog

MARCH 18, 2025

SQL is one of the key languages widely used across businesses, and it requires an understanding of databases and table metadata. This can be overwhelming for nontechnical users who lack proficiency in SQL. This application allows users to ask questions in natural language and then generates a SQL query for the users request.

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AWS Machine Learning Blog

FEBRUARY 28, 2024

Structured Query Language (SQL) is a complex language that requires an understanding of databases and metadata. Today, generative AI can enable people without SQL knowledge. This generative AI task is called text-to-SQL, which generates SQL queries from natural language processing (NLP) and converts text into semantically correct SQL.

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AWS Machine Learning Blog

NOVEMBER 20, 2024

In this post, we explore how you can use Amazon Q Business , the AWS generative AI-powered assistant, to build a centralized knowledge base for your organization, unifying structured and unstructured datasets from different sources to accelerate decision-making and drive productivity. In this post, we use IAM Identity Center as the SAML 2.0-aligned

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AWS Machine Learning Blog

OCTOBER 24, 2024

Prerequisites Before you begin, make sure you have the following prerequisites in place: An AWS account and role with the AWS Identity and Access Management (IAM) privileges to deploy the following resources: IAM roles. Basic knowledge of a SQL query editor. A provisioned or serverless Amazon Redshift data warehouse.

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AWS Machine Learning Blog

AUGUST 8, 2024

As one of the largest AWS customers, Twilio engages with data, artificial intelligence (AI), and machine learning (ML) services to run their daily workloads. Managing and retrieving the right information can be complex, especially for data analysts working with large data lakes and complex SQL queries.

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Data Science Blog

SEPTEMBER 19, 2023

The following Terraform script will create an Azure Resource Group, a SQL Server, and a SQL Database. AWS CloudFormation is a service offered by Amazon Web Services (AWS) that allows you to define cloud infrastructure in JSON or YAML templates. appeared first on Data Science Blog.

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AWS Machine Learning Blog

AUGUST 1, 2024

To address this challenge, AWS recently announced the preview of Amazon Bedrock Custom Model Import , a feature that you can use to import customized models created in other environments—such as Amazon SageMaker , Amazon Elastic Compute Cloud (Amazon EC2) instances, and on premises—into Amazon Bedrock.

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Flipboard

APRIL 13, 2023

At AWS, we have played a key role in democratizing ML and making it accessible to anyone who wants to use it, including more than 100,000 customers of all sizes and industries. AWS has the broadest and deepest portfolio of AI and ML services at all three layers of the stack. Today’s FMs, such as the large language models (LLMs) GPT3.5

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AWS Machine Learning Blog

MAY 30, 2024

Because Amazon Bedrock is serverless, you don’t have to manage infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with. AWS Prototyping developed an AWS Cloud Development Kit (AWS CDK) stack for deployment following AWS best practices.

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AWS Machine Learning Blog

FEBRUARY 13, 2025

Amazon Bedrock offers a serverless experience, so you can get started quickly, privately customize FMs with your own data, and integrate and deploy them into your applications using Amazon Web Services (AWS) services without having to manage infrastructure. AWS Lambda The API is a Fastify application written in TypeScript.

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AWS Machine Learning Blog

JUNE 20, 2024

The data is stored in a data lake and retrieved by SQL using Amazon Athena. The following figure shows a search query that was translated to SQL and run. Data is normally stored in databases, and can be queried using the most common query language, SQL. Constructing SQL queries from natural language isn’t a simple task.

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AWS Machine Learning Blog

SEPTEMBER 13, 2024

Managing cloud costs and understanding resource usage can be a daunting task, especially for organizations with complex AWS deployments. AWS Cost and Usage Reports (AWS CUR) provides valuable data insights, but interpreting and querying the raw data can be challenging.

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AWS Machine Learning Blog

MARCH 12, 2024

In this post, we explore what an audience overlap analysis is, discuss the current technical approaches and their challenges, and illustrate how you can run secure audience overlap analysis using AWS Clean Rooms. With AWS Clean Rooms, you can create a data clean room in minutes and collaborate with your partners to generate unique insights.

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Flipboard

JUNE 26, 2023

In this post, we look at how we can use AWS Glue and the AWS Lake Formation ML transform FindMatches to harmonize (deduplicate) customer data coming from different sources to get a complete customer profile to be able to provide better customer experience. Run the AWS Glue ML transform job.

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AWS Machine Learning Blog

DECEMBER 6, 2023

To overcome these limitations, we propose a solution that combines RAG with metadata and entity extraction, SQL querying, and LLM agents, as described in the following sections. Typically, these analytical operations are done on structured data, using tools such as pandas or SQL engines.

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