2013, AWS and ML - Data Science Current

Your guide to generative AI and ML at AWS re:Invent 2023

AWS Machine Learning Blog

NOVEMBER 22, 2023

Yes, the AWS re:Invent season is upon us and as always, the place to be is Las Vegas! Now all you need is some guidance on generative AI and machine learning (ML) sessions to attend at this twelfth edition of re:Invent. are the sessions dedicated to AWS DeepRacer ! are the sessions dedicated to AWS DeepRacer !

AWS

AWS ML ML AI

Navigating tomorrow: Role of AI and ML in information technology

Dataconomy

FEBRUARY 6, 2024

With the ability to analyze a vast amount of data in real-time, identify patterns, and detect anomalies, AI/ML-powered tools are enhancing the operational efficiency of businesses in the IT sector. Why does AI/ML deserve to be the future of the modern world? Let’s understand the crucial role of AI/ML in the tech industry.

ML

ML ML Machine Learning Machine Learning

Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker

AWS Machine Learning Blog

MARCH 15, 2024

Many organizations are implementing machine learning (ML) to enhance their business decision-making through automation and the use of large distributed datasets. With increased access to data, ML has the potential to provide unparalleled business insights and opportunities.

AWS

AWS ML ML Machine Learning

Webinars

Apache Airflow®: The Ultimate Guide to DAG Writing

The 2nd Generation of Innovation Management: A Survival Guide

MORE WEBINARS

Learn how to assess the risk of AI systems

Flipboard

NOVEMBER 28, 2023

While it might be easier to start looking at an individual machine learning (ML) model and the associated risks in isolation, it’s important to consider the details of the specific application of such a model and the corresponding use case as part of a complete AI system. In this post, we focus on AI system risk, primarily.

AI

AI AI AWS ML

Create a multimodal chatbot tailored to your unique dataset with Amazon Bedrock FMs

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.

AWS

AWS AI AI System Architecture

Designing generative AI workloads for resilience

AWS Machine Learning Blog

FEBRUARY 1, 2024

Consider the following picture, which is an AWS view of the a16z emerging application stack for large language models (LLMs). This includes native AWS services like Amazon OpenSearch Service and Amazon Aurora. Is your vector database highly available in a single AWS Region? Vector database features built into other services.

AWS

AWS AI AI Database

Reinventing the data experience: Use generative AI and modern data architecture to unlock insights

AWS Machine Learning Blog

JUNE 13, 2023

Overall, implementing a modern data architecture and generative AI techniques with AWS is a promising approach for gleaning and disseminating key insights from diverse, expansive data at an enterprise scale. AWS also offers foundation models through Amazon SageMaker JumpStart as Amazon SageMaker endpoints.

Database

Database SQL AWS AI

Monitor embedding drift for LLMs deployed from Amazon SageMaker JumpStart

AWS Machine Learning Blog

FEBRUARY 2, 2024

The embeddings are captured in Amazon Simple Storage Service (Amazon S3) via Amazon Kinesis Data Firehose , and we run a combination of AWS Glue extract, transform, and load (ETL) jobs and Jupyter notebooks to perform the embedding analysis. For more information about AWS CDK installation, refer to Getting started with the AWS CDK.

AWS

AWS Clustering ETL Database

Generating value from enterprise data: Best practices for Text2SQL and generative AI

AWS Machine Learning Blog

JANUARY 4, 2024

In this pattern, we use Retrieval Augmented Generation using vector embeddings stores, like Amazon Titan Embeddings or Cohere Embed , on Amazon Bedrock from a central data catalog, like AWS Glue Data Catalog , of databases within an organization. In entered the Big Data space in 2013 and continues to explore that area.

SQL

SQL Database AI AI

Accelerate time to business insights with the Amazon SageMaker Data Wrangler direct connection to Snowflake

AWS Machine Learning Blog

JUNE 23, 2023

Amazon SageMaker Data Wrangler is a single visual interface that reduces the time required to prepare data and perform feature engineering from weeks to minutes with the ability to select and clean data, create features, and automate data preparation in machine learning (ML) workflows without writing any code.

ML

ML ML Database AWS

Generate financial industry-specific insights using generative AI and in-context fine-tuning

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). Solutions Architect at AWS. Varun Mehta is a Sr.

SQL

SQL AWS AI AI

Use a generative AI foundation model for summarization and question answering using your own data

Flipboard

JULY 19, 2023

As part of the post-processing, an AWS Lambda function inserts special markers into the text indicating page boundaries. Another Lambda function picks up that message and starts an Amazon Elastic Container Service (Amazon ECS) AWS Fargate task. About the author Randy DeFauw is a Senior Principal Solutions Architect at AWS.

AWS

AWS Database AI AI

Containerization of Machine Learning Applications

Heartbeat

DECEMBER 27, 2023

However, the emergence of the open-source Docker engine by Solomon Hykes in 2013 accelerated the adoption of the technology. The machine learning (ML) lifecycle defines steps to derive values to meet business objectives using ML and artificial intelligence (AI). What is Docker? Docker is the most popular container technology.

Machine Learning

Machine Learning Machine Learning Python ML

Automate document validation and fraud detection in the mortgage underwriting process using AWS AI services: Part 1

AWS Machine Learning Blog

MAY 24, 2023

In this three-part series, we present a solution that demonstrates how you can automate detecting document tampering and fraud at scale using AWS AI and machine learning (ML) services for a mortgage underwriting use case. In the following sections, we discuss the stages of the process in detail.

AWS

AWS ML ML AI

How SnapLogic built a text-to-pipeline application with Amazon Bedrock to translate business intent into action

Flipboard

NOVEMBER 24, 2023

In this post, we show you how SnapLogic , an AWS customer, used Amazon Bedrock to power their SnapGPT product through automated creation of these complex DSL artifacts from human language. SnapLogic background SnapLogic is an AWS customer on a mission to bring enterprise automation to the world.

Database

Database AWS ETL SQL

Data Science Current

Your guide to generative AI and ML at AWS re:Invent 2023

Navigating tomorrow: Role of AI and ML in information technology

Webinars

Trending Sources

Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker

Webinars

Learn how to assess the risk of AI systems

Create a multimodal chatbot tailored to your unique dataset with Amazon Bedrock FMs

Designing generative AI workloads for resilience

Reinventing the data experience: Use generative AI and modern data architecture to unlock insights

Monitor embedding drift for LLMs deployed from Amazon SageMaker JumpStart

Generating value from enterprise data: Best practices for Text2SQL and generative AI

Accelerate time to business insights with the Amazon SageMaker Data Wrangler direct connection to Snowflake

Generate financial industry-specific insights using generative AI and in-context fine-tuning

Use a generative AI foundation model for summarization and question answering using your own data

Containerization of Machine Learning Applications

Automate document validation and fraud detection in the mortgage underwriting process using AWS AI services: Part 1

How SnapLogic built a text-to-pipeline application with Amazon Bedrock to translate business intent into action

Stay Connected