Flipboard

JUNE 26, 2023

These techniques utilize various machine learning (ML) based approaches. 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.

AWS 123

AWS ML ML ETL 123

AWS Machine Learning Blog

DECEMBER 4, 2024

Building generative AI applications presents significant challenges for organizations: they require specialized ML expertise, complex infrastructure management, and careful orchestration of multiple services. You can obtain the SageMaker Unified Studio URL for your domains by accessing the AWS Management Console for Amazon DataZone.

AWS 107

AWS AI AI SQL 107

Data Science Dojo

OCTOBER 31, 2024

Applied Machine Learning Scientist Description : Applied ML Scientists focus on translating algorithms into scalable, real-world applications. Demand for applied ML scientists remains high, as more companies focus on AI-driven solutions for scalability. Familiarity with machine learning, algorithms, and statistical modeling.

Data Science 195

Data Science Data Scientist Machine Learning Machine Learning 195

AWS Machine Learning Blog

NOVEMBER 12, 2024

You may check out additional reference notebooks on aws-samples for how to use Meta’s Llama models hosted on Amazon Bedrock. You can implement these steps either from the AWS Management Console or using the latest version of the AWS Command Line Interface (AWS CLI).

SQL 120

SQL AWS AI AI 120

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. Learn more about the AWS zero-ETL future with newly launched AWS databases integrations with Amazon Redshift.

AWS 139

AWS Data Warehouse ETL SQL 139

Flipboard

APRIL 13, 2023

The seeds of a machine learning (ML) paradigm shift have existed for decades, but with the ready availability of scalable compute capacity, a massive proliferation of data, and the rapid advancement of ML technologies, customers across industries are transforming their businesses.

AWS 182

AWS ML ML AI 182

Flipboard

JANUARY 24, 2025

Use the AWS generative AI scoping framework to understand the specific mix of the shared responsibility for the security controls applicable to your application. The following figure of the AWS Generative AI Security Scoping Matrix summarizes the types of models for each scope.

AWS 160

AWS AI AI Data Warehouse 160

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.

SQL 127

SQL AWS Database Data Scientist 127

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.

SQL 142

SQL AWS Database ML 142

AWS Machine Learning Blog

AUGUST 12, 2024

Instead, organizations are increasingly looking to take advantage of transformative technologies like machine learning (ML) and artificial intelligence (AI) to deliver innovative products, improve outcomes, and gain operational efficiencies at scale.

ML 124

ML ML AWS AI 124

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.

SQL 127

SQL AWS ML ML 127

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. Data is the foundational layer for all generative AI and ML applications. The following diagram illustrates the solution architecture.

SQL 128

SQL Data Lakes Data Analyst AWS 128

AWS Machine Learning Blog

MARCH 8, 2023

Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. Store your Snowflake account credentials in AWS Secrets Manager.

ML 133

ML ML AWS Python 133

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.

AWS 130

AWS SQL Database AI 130

Flipboard

DECEMBER 11, 2024

Second, because data, code, and other development artifacts like machine learning (ML) models are stored within different services, it can be cumbersome for users to understand how they interact with each other and make changes. With the SQL editor, you can query data lakes, databases, data warehouses, and federated data sources.

SQL 160

SQL AWS Data Lakes AI 160

AWS Machine Learning Blog

FEBRUARY 12, 2025

Prerequisites To build the solution yourself, there are the following prerequisites: You need an AWS account with an AWS Identity and Access Management (IAM) role that has permissions to manage resources created as part of the solution (for example AmazonSageMakerFullAccess and AmazonS3FullAccess ).

AWS 79

AWS AI AI SQL 79

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.

ML 123

ML ML AWS Data Warehouse 123

AWS Machine Learning Blog

SEPTEMBER 18, 2024

What Zeta has accomplished in AI/ML In the fast-evolving landscape of digital marketing, Zeta Global stands out with its groundbreaking advancements in artificial intelligence. Hosted on Amazon ECS with tasks run on Fargate, this platform streamlines the end-to-end ML workflow, from data ingestion to model deployment.

AWS 122

AWS Machine Learning Machine Learning ML 122

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.

SQL 127

SQL AWS Analytics Analytics 127

AWS Machine Learning Blog

SEPTEMBER 19, 2023

Quantitative modeling and forecasting – Generative models can synthesize large volumes of financial data to train machine learning (ML) models for applications like stock price forecasting, portfolio optimization, risk modeling, and more. Multi-modal models that understand diverse data sources can provide more robust forecasts.

AWS 134

AWS AI AI ML 134

Hacker News

NOVEMBER 19, 2024

Here are a few of the things that you might do as an AI Engineer at TigerEye: - Design, develop, and validate statistical models to explain past behavior and to predict future behavior of our customers’ sales teams - Own training, integration, deployment, versioning, and monitoring of ML components - Improve TigerEye’s existing metrics collection and (..)

Computer Science 144

Computer Science Computer Science ML ML 144

Flipboard

DECEMBER 18, 2024

Amazon OpenSearch Service Amazon OpenSearch Service is a fully managed service that simplifies the deployment, operation, and scaling of OpenSearch in the AWS Cloud to provide powerful search and analytics capabilities. Teams can use OpenSearch Service ML connectors which facilitate access to models hosted on third-party ML platforms.

K-nearest Neighbors 121

K-nearest Neighbors AWS ML ML 121

AWS Machine Learning Blog

AUGUST 30, 2024

With the rapid growth of generative artificial intelligence (AI), many AWS customers are looking to take advantage of publicly available foundation models (FMs) and technologies. Meta Llama 3’s capabilities enhance accuracy and efficiency in understanding and generating SQL queries from natural language inputs.

SQL 91

SQL AWS Database AI 91

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.

ML 126

ML ML AWS SQL 126

AWS Machine Learning Blog

MAY 1, 2024

To retrieve data from database, you can use foundation models (FMs) offered by Amazon Bedrock, converting text into SQL queries with specified constraints. Virginia) AWS Region. The diagram details a comprehensive AWS Cloud-based setup within a specific Region, using multiple AWS services.

AWS 131

AWS Machine Learning Machine Learning SQL 131

AWS Machine Learning Blog

MARCH 8, 2023

MPII is using a machine learning (ML) bid optimization engine to inform upstream decision-making processes in power asset management and trading. MPII’s bid optimization engine solution uses ML models to generate optimal bids for participation in different markets. Data comes from disparate sources in a number of formats.

AWS 101

AWS Machine Learning Machine Learning Analytics 101

AWS Machine Learning Blog

JANUARY 4, 2024

One such area that is evolving is using natural language processing (NLP) to unlock new opportunities for accessing data through intuitive SQL queries. The primary goal is to automatically generate SQL queries from natural language text. What percentage of customers are from each region?”

SQL 140

SQL Database AI AI 140

AWS Machine Learning Blog

SEPTEMBER 27, 2024

Solution overview Amazon SageMaker is a fully managed service that helps developers and data scientists build, train, and deploy machine learning (ML) models. The workforce created a bounding box around stay wires and insulators and the output was subsequently used to train an ML model.

AWS 120

AWS Data Lakes ML ML 120

AWS Machine Learning Blog

AUGUST 22, 2024

Snowflake Arctic is a family of enterprise-grade large language models (LLMs) built by Snowflake to cater to the needs of enterprise users, exhibiting exceptional capabilities (as shown in the following benchmarks ) in SQL querying, coding, and accurately following instructions.

SQL 130

SQL AWS Python ML 130

AWS Machine Learning Blog

NOVEMBER 27, 2023

In this post, we demonstrate how business analysts and citizen data scientists can create machine learning (ML) models, without any code, in Amazon SageMaker Canvas and deploy trained models for integration with Salesforce Einstein Studio to create powerful business applications. For Callback URL , enter [link].studio.sagemaker.aws/canvas/default/lab

ML 105

ML ML AWS SQL 105

AWS Machine Learning Blog

APRIL 19, 2023

Businesses are increasingly using machine learning (ML) to make near-real-time decisions, such as placing an ad, assigning a driver, recommending a product, or even dynamically pricing products and services. As a result, some enterprises have spent millions of dollars inventing their own proprietary infrastructure for feature management.

ML 98

ML ML Apache Kafka SQL 98

AWS Machine Learning Blog

MARCH 14, 2024

We formulated a text-to-SQL approach where by a user’s natural language query is converted to a SQL statement using an LLM. The SQL is run by Amazon Athena to return the relevant data. Our final solution is a combination of these text-to-SQL and text-RAG approaches. The following table contains some example responses.

SQL 132

SQL AWS AI AI 132

IBM Journey to AI blog

MARCH 14, 2024

The solution: IBM databases on AWS To solve for these challenges, IBM’s portfolio of SaaS database solutions on Amazon Web Services (AWS), enables enterprises to scale applications, analytics and AI across the hybrid cloud landscape. Let’s delve into the database portfolio from IBM available on AWS. 

AWS 93

AWS Database ETL AI 93

AWS Machine Learning Blog

AUGUST 20, 2024

For example, SQL queries can be complex and unintuitive for non-technical users. Handling complex queries involving multiple tables, joins, and aggregations makes it difficult to interpret user intent and translate it into correct SQL operations. Amazon Q Business analyzes intent, accesses data sources, and generates the SQL query.

SQL 120

SQL AWS Database Natural Language Processing 120

AWS Machine Learning Blog

APRIL 24, 2024

With AWS generative AI services like Amazon Bedrock , developers can create systems that expertly manage and respond to user requests. It is hosted on Amazon Elastic Container Service (Amazon ECS) with AWS Fargate , and it is accessed using an Application Load Balancer. It serves as the data source to the knowledge base.

AWS 138

AWS AI AI SQL 138

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.

AWS 98

AWS Data Lakes Clustering Data Preparation 98

AWS Machine Learning Blog

JUNE 5, 2023

Data is the foundation for machine learning (ML) algorithms. Canvas provides connectors to AWS data sources such as Amazon Simple Storage Service (Amazon S3), Athena, and Amazon Redshift. In this post, we describe how to query Parquet files with Athena using AWS Lake Formation and use the output Canvas to train a model.

Machine Learning 98

Machine Learning Machine Learning AWS Data Lakes 98