AWS, Books and Clustering - Data Science Current

Building a Data Pipeline with PySpark and AWS

Analytics Vidhya

AUGUST 3, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Apache Spark is a framework used in cluster computing environments. The post Building a Data Pipeline with PySpark and AWS appeared first on Analytics Vidhya.

Data Pipeline

Data Pipeline AWS Clustering Data Science

Integrate HyperPod clusters with Active Directory for seamless multi-user login

AWS Machine Learning Blog

APRIL 22, 2024

Amazon SageMaker HyperPod is purpose-built to accelerate foundation model (FM) training, removing the undifferentiated heavy lifting involved in managing and optimizing a large training compute cluster. In this solution, HyperPod cluster instances use the LDAPS protocol to connect to the AWS Managed Microsoft AD via an NLB.

Clustering

Clustering AWS Machine Learning Machine Learning

Boost your forecast accuracy with time series clustering

AWS Machine Learning Blog

APRIL 4, 2023

AWS provides various services catered to time series data that are low code/no code, which both machine learning (ML) and non-ML practitioners can use for building ML solutions. In this post, we seek to separate a time series dataset into individual clusters that exhibit a higher degree of similarity between its data points and reduce noise.

Clustering

Clustering ML ML AWS

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Exploring architectural choices: Options for running IBM TRIRIGA Application Suite on AWS with Red Hat OpenShift

IBM Journey to AI blog

APRIL 3, 2024

Shared data allows occupants to make service requests and book rooms, right-size portfolios and increase the efficiency of lease administration, capital projects and more. In this blog post, we walk through the recommended options for running IBM TAS on Amazon Web Services (AWS).

AWS

AWS Clustering AI AI

How Amazon Search M5 saved 30% for LLM training cost by using AWS Trainium

AWS Machine Learning Blog

NOVEMBER 22, 2023

From the earliest days, Amazon has used ML for various use cases such as book recommendations, search, and fraud detection. Last year, AWS launched its AWS Trainium accelerators, which optimize performance per cost for developing and building next generation DL models.

AWS

AWS ML ML Deep Learning

Amazon SageMaker model parallel library now accelerates PyTorch FSDP workloads by up to 20%

AWS Machine Learning Blog

DECEMBER 22, 2023

As a result, machine learning practitioners must spend weeks of preparation to scale their LLM workloads to large clusters of GPUs. Integrating tensor parallelism to enable training on massive clusters This release of SMP also expands PyTorch FSDP’s capabilities to include tensor parallelism techniques.

Clustering

Clustering Deep Learning Deep Learning AWS

How BigBasket improved AI-enabled checkout at their physical stores using Amazon SageMaker

AWS Machine Learning Blog

FEBRUARY 13, 2024

Model training was accelerated by 50% through the use of the SMDDP library, which includes optimized communication algorithms designed specifically for AWS infrastructure. For SageMaker distributed training, the instances need to be in the same AWS Region and Availability Zone. days in AWS vs. 9 days on their legacy platform).

AWS

AWS AI AI ML

CBRE and AWS perform natural language queries of structured data using Amazon Bedrock

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

AWS SQL Database AI

Implement exact match with Amazon Lex QnAIntent

AWS Machine Learning Blog

JUNE 24, 2024

In this post, we walk through how to set up and configure an OpenSearch Service cluster as the knowledge base for your Amazon Lex QnAIntent. Prerequisites Before creating an OpenSearch Service cluster, you need to create an Amazon Lex V2 bot. In an enterprise environment, you typically launch your OpenSearch Service cluster in a VPC.

Clustering

Clustering AWS Artificial Intelligence Artificial Intelligence

Develop and train large models cost-efficiently with Metaflow and AWS Trainium

AWS Machine Learning Blog

APRIL 29, 2024

For AWS and Outerbounds customers, the goal is to build a differentiated machine learning and artificial intelligence (ML/AI) system and reliably improve it over time. First, the AWS Trainium accelerator provides a high-performance, cost-effective, and readily available solution for training and fine-tuning large models.

AWS

AWS ML ML Python

Build generative AI chatbots using prompt engineering with Amazon Redshift and Amazon Bedrock

AWS Machine Learning Blog

FEBRUARY 14, 2024

We build a personalized generative AI travel itinerary planner as part of this example and demonstrate how we can personalize a travel itinerary for a user based on their booking and user profile data stored in Amazon Redshift. An SSL certificate created and imported into AWS Certificate Manager (ACM).

AWS

AWS AI AI Database

Software infrastructure 2.0: a wishlist

Hacker News

APRIL 18, 2021

A touchscreen interface that's super laggy, or an appointment booking app that forces you to go in and out of possible dates and fill in all information before it tells you if it's available. The word cluster is an anachronism to an end-user in the cloud! We are, like what, 10 years into the cloud adoption?

Database

Database AWS Clustering

Introduction to GitHub Actions for Python Projects

PyImageSearch

SEPTEMBER 30, 2024

Deployment Server Tools: Kubernetes, Docker Swarm, AWS CodeDeploy Purpose: Automates the deployment of applications to staging or production environments. Orchestration Tools: Kubernetes, Docker Swarm Purpose: Manages the deployment, scaling, and operation of application containers across clusters of hosts. Download the code!

Python

Python Deep Learning Deep Learning AWS

Importance of Case Studies

Mlearning.ai

JULY 25, 2023

Case Study Book in Progress! After completed these case studies and participating in the recent rapid advancement of Data Science technologies, especially learning how to do Data Science on many cloud platforms (Azure, AWS, GCP, a little IBM). Happy Practicing! ? ? D onate | ? GitHub | ?

Data Science

Data Science Clustering Analytics Analytics

Scaling Large Language Model (LLM) training with Amazon EC2 Trn1 UltraClusters

Flipboard

FEBRUARY 16, 2023

Modern model pre-training often calls for larger cluster deployment to reduce time and cost. In October 2022, we launched Amazon EC2 Trn1 Instances , powered by AWS Trainium , which is the second generation machine learning accelerator designed by AWS. The following diagram shows an example.

Clustering

Clustering AWS Deep Learning Deep Learning

A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

SEPTEMBER 11, 2024

The following figure illustrates the idea of a large cluster of GPUs being used for learning, followed by a smaller number for inference. Examples of other PBAs now available include AWS Inferentia and AWS Trainium , Google TPU, and Graphcore IPU. Suppliers of data center GPUs include NVIDIA, AMD, Intel, and others.

AWS

AWS ML ML Clustering

Getting started with Amazon Titan Text Embeddings

AWS Machine Learning Blog

JANUARY 31, 2024

Amazon Titan Text Embeddings is a text embeddings model that converts natural language text—consisting of single words, phrases, or even large documents—into numerical representations that can be used to power use cases such as search, personalization, and clustering based on semantic similarity. Nitin Eusebius is a Sr.

Natural Language Processing

Natural Language Processing AWS Machine Learning Machine Learning

How Reveal’s Logikcull used Amazon Comprehend to detect and redact PII from legal documents at scale

AWS Machine Learning Blog

NOVEMBER 1, 2023

PII Detected tagged documents are fed into Logikcull’s search index cluster for their users to quickly identify documents that contain PII entities. The request is handled by Logikcull’s application servers hosted on Amazon EC2 and the servers communicates with the search index cluster to find the documents.

AWS

AWS Machine Learning Machine Learning ML

CRISPR-Cas9 guide RNA efficiency prediction with efficiently tuned models in Amazon SageMaker

AWS Machine Learning Blog

SEPTEMBER 16, 2024

The clustered regularly interspaced short palindromic repeat (CRISPR) technology holds the promise to revolutionize gene editing technologies, which is transformative to the way we understand and treat diseases. We also provided code that can help you jumpstart your biology applications in AWS.

Natural Language Processing

Natural Language Processing AWS Deep Learning Deep Learning

Gemma is now available in Amazon SageMaker JumpStart

AWS Machine Learning Blog

MARCH 13, 2024

Because the models are hosted and deployed on AWS, your data, whether used for evaluating the model or using it at scale, is never shared with third parties. In the AWS Management Console for SageMaker Studio, go to SageMaker JumpStart under Prebuilt and automated solutions. Assistant: Certainly!

Machine Learning

Machine Learning Machine Learning Algorithm Python

Best Practices for Managing Computer Vision Projects

DagsHub

MARCH 19, 2024

Tesla, for instance, relies on a cluster of NVIDIA A100 GPUs to train their vision-based autonomous driving algorithms. But, if you're looking to deploy your computer vision projects in the cloud, some of the cloud services tailored for computer vision projects are Google Cloud Vision AI and AWS Rekognition. How Do You Measure Success?

Algorithm

Algorithm Deep Learning Deep Learning Data Engineering

Welcome to a New Era of Building in the Cloud with Generative AI on AWS

AWS Machine Learning Blog

NOVEMBER 30, 2023

The number of companies launching generative AI applications on AWS is substantial and building quickly, including adidas, Booking.com, Bridgewater Associates, Clariant, Cox Automotive, GoDaddy, and LexisNexis Legal & Professional, to name just a few. Innovative startups like Perplexity AI are going all in on AWS for generative AI.

AWS

AWS AI AI ML

MLOps and DevOps: Why Data Makes It Different

O'Reilly Media

OCTOBER 19, 2021

Adapted from the book Effective Data Science Infrastructure. Prior to the cloud, setting up and operating a cluster that can handle workloads like this would have been a major technical challenge. Today, a number of cloud-based, auto-scaling systems are easily available, such as AWS Batch. Foundational Infrastructure Layers.

ML

ML ML Data Scientist AWS

Zero-shot prompting for the Flan-T5 foundation model in Amazon SageMaker JumpStart

AWS Machine Learning Blog

APRIL 3, 2023

nn[”yes”, ”no”] yes question answering Answer based on context:nn The newest and most innovative Kindle yet lets you take notes on millions of books and documents, write lists and journals, and more. He works with Machine Learning Startups to build and deploy AI/ML applications on AWS.

Natural Language Processing

Natural Language Processing Machine Learning Machine Learning Algorithm

Perform generative AI-powered data prep and no-code ML over any size of data using Amazon SageMaker Canvas

AWS Machine Learning Blog

AUGUST 15, 2024

Afterward, you need to manage complex clusters to process and train your ML models over these large-scale datasets. Solutions Architect at AWS. He works closely with enterprise customers building data lakes and analytical applications on the AWS platform. Peter Chung is a Solutions Architect serving enterprise customers at AWS.

ML

ML ML Data Preparation AWS

Learnings From Building the ML Platform at Mailchimp

The MLOps Blog

OCTOBER 3, 2023

For example, you can use BigQuery , AWS , or Azure. It can be a cluster run by Kubernetes or maybe something else. How awful are they?” In terms of the interaction, ideally, the data scientists shouldn’t have to be setting up infrastructure like a Spark cluster. They’re terrible people.

ML

ML ML Data Scientist Machine Learning

Introducing Amazon EKS support in Amazon SageMaker HyperPod

AWS Machine Learning Blog

SEPTEMBER 11, 2024

This capability allows for the seamless addition of SageMaker HyperPod managed compute to EKS clusters, using automated node and job resiliency features for foundation model (FM) development. FMs are typically trained on large-scale compute clusters with hundreds or thousands of accelerators.

Clustering

Clustering AWS ML ML

Enel automates large-scale power grid asset management and anomaly detection using Amazon SageMaker

AWS Machine Learning Blog

JULY 20, 2023

This allows the clustering of ROIs referring to the same pole. During his spare time he likes playing golf with friends and travelling abroad with only fly and drive bookings. He has worked on projects in different domains, including MLOps, Computer Vision, NLP, and involving a broad set of AWS services.

ML

ML ML Machine Learning Machine Learning

Techniques for automatic summarization of documents using language models

Flipboard

DECEMBER 6, 2023

Click here to open the AWS console and follow along. The model then uses a clustering algorithm to group the sentences into clusters. The sentences that are closest to the center of each cluster are selected to form the summary. To use one of these models, AWS offers the fully managed service Amazon Bedrock.

AWS

AWS Clustering Artificial Intelligence Artificial Intelligence

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

Unlocking generative AI for enterprises: How SnapLogic powers their low-code Agent Creator using Amazon Bedrock

AWS Machine Learning Blog

OCTOBER 23, 2024

SnapLogic uses Amazon Bedrock to build its platform, capitalizing on the proximity to data already stored in Amazon Web Services (AWS). To address customers’ requirements about data privacy and sovereignty, SnapLogic deploys the data plane within the customer’s VPC on AWS.

AI

AI AI AWS Database

Techniques for reducing costs in LLM architectures

DagsHub

JULY 15, 2024

They can engage users in natural dialogue, provide customer support, answer FAQs, and assist with booking or shopping decisions. Whether you are opting to fine-tune on a local machine or the cloud, predominant factors related to cost will be fine-tuning time, GPU clusters, and storage.

Azure

Azure AI AI Database

Deploy thousands of model ensembles with Amazon SageMaker multi-model endpoints on GPU to minimize your hosting costs

AWS Machine Learning Blog

AUGUST 8, 2023

AWS, in its dedication to help customers achieve the highest saving, has continuously innovated not only in pricing options and cost-optimization proactive services , but also in launching cost savings features like multi-model endpoints (MMEs). Outside of work, he enjoys reading books, fiddling with the guitar, and making pizza.

Deep Learning

Deep Learning Deep Learning AWS ML

Data Science Current

Building a Data Pipeline with PySpark and AWS

Integrate HyperPod clusters with Active Directory for seamless multi-user login

Webinars

Trending Sources

Boost your forecast accuracy with time series clustering

Webinars

Exploring architectural choices: Options for running IBM TRIRIGA Application Suite on AWS with Red Hat OpenShift

How Amazon Search M5 saved 30% for LLM training cost by using AWS Trainium

Amazon SageMaker model parallel library now accelerates PyTorch FSDP workloads by up to 20%

How BigBasket improved AI-enabled checkout at their physical stores using Amazon SageMaker

CBRE and AWS perform natural language queries of structured data using Amazon Bedrock

Implement exact match with Amazon Lex QnAIntent

Develop and train large models cost-efficiently with Metaflow and AWS Trainium

Build generative AI chatbots using prompt engineering with Amazon Redshift and Amazon Bedrock

Software infrastructure 2.0: a wishlist

Introduction to GitHub Actions for Python Projects

Importance of Case Studies

Scaling Large Language Model (LLM) training with Amazon EC2 Trn1 UltraClusters

A review of purpose-built accelerators for financial services

Getting started with Amazon Titan Text Embeddings

How Reveal’s Logikcull used Amazon Comprehend to detect and redact PII from legal documents at scale

CRISPR-Cas9 guide RNA efficiency prediction with efficiently tuned models in Amazon SageMaker

Gemma is now available in Amazon SageMaker JumpStart

Best Practices for Managing Computer Vision Projects

Welcome to a New Era of Building in the Cloud with Generative AI on AWS

MLOps and DevOps: Why Data Makes It Different

Zero-shot prompting for the Flan-T5 foundation model in Amazon SageMaker JumpStart

Perform generative AI-powered data prep and no-code ML over any size of data using Amazon SageMaker Canvas

Learnings From Building the ML Platform at Mailchimp

Introducing Amazon EKS support in Amazon SageMaker HyperPod

Enel automates large-scale power grid asset management and anomaly detection using Amazon SageMaker

Techniques for automatic summarization of documents using language models

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

Unlocking generative AI for enterprises: How SnapLogic powers their low-code Agent Creator using Amazon Bedrock

Techniques for reducing costs in LLM architectures

Deploy thousands of model ensembles with Amazon SageMaker multi-model endpoints on GPU to minimize your hosting costs

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