AWS, Big Data and Download - Data Science Current

Build and deploy a UI for your generative AI applications with AWS and Python

AWS Machine Learning Blog

NOVEMBER 6, 2024

Traditionally, building frontend and backend applications has required knowledge of web development frameworks and infrastructure management, which can be daunting for those with expertise primarily in data science and machine learning. Choose the us-east-1 AWS Region from the top right corner. Choose Manage model access.

AWS

AWS Python AI AI

Search enterprise data assets using LLMs backed by knowledge graphs

Flipboard

NOVEMBER 27, 2024

Enterprises are facing challenges in accessing their data assets scattered across various sources because of increasing complexities in managing vast amount of data. Traditional search methods often fail to provide comprehensive and contextual results, particularly for unstructured data or complex queries.

AWS

AWS Database ML ML

Fast and cost-effective LLaMA 2 fine-tuning with AWS Trainium

AWS Machine Learning Blog

OCTOBER 5, 2023

In this post, we walk through how to fine-tune Llama 2 on AWS Trainium , a purpose-built accelerator for LLM training, to reduce training times and costs. We review the fine-tuning scripts provided by the AWS Neuron SDK (using NeMo Megatron-LM), the various configurations we used, and the throughput results we saw.

AWS

AWS Machine Learning Machine Learning Deep Learning

Unify structured data in Amazon Aurora and unstructured data in Amazon S3 for insights using Amazon Q

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. Complete the following steps: Choose an AWS Region Amazon Q supports (for this post, we use the us-east-1 Region). aligned identity provider (IdP).

Database

Database AWS SQL ETL

Harmonize data using AWS Glue and AWS Lake Formation FindMatches ML to build a customer 360 view

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.

AWS

AWS ML ML ETL

Frugality meets Accuracy: Cost-efficient training of GPT NeoX and Pythia models with AWS Trainium

AWS Machine Learning Blog

DECEMBER 12, 2023

In this post, we’ll summarize training procedure of GPT NeoX on AWS Trainium , a purpose-built machine learning (ML) accelerator optimized for deep learning training. M tokens/$) trained such models with AWS Trainium without losing any model quality. We’ll outline how we cost-effectively (3.2 billion in Pythia.

AWS

AWS Machine Learning Machine Learning Deep Learning

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

AWS Machine Learning Blog

MARCH 15, 2024

The need for federated learning in healthcare Healthcare relies heavily on distributed data sources to make accurate predictions and assessments about patient care. Limiting the available data sources to protect privacy negatively affects result accuracy and, ultimately, the quality of patient care.

AWS

AWS ML ML Machine Learning

NeMo Retriever Llama 3.2 text embedding and reranking NVIDIA NIM microservices now available in Amazon SageMaker JumpStart

AWS Machine Learning Blog

MARCH 18, 2025

With this launch, you can now deploy NVIDIAs optimized reranking and embedding models to build, experiment, and responsibly scale your generative AI ideas on AWS. As part of NVIDIA AI Enterprise available in AWS Marketplace , NIM is a set of user-friendly microservices designed to streamline and accelerate the deployment of generative AI.

AWS

AWS AI AI Computer Science

Llama 4 family of models from Meta are now available in SageMaker JumpStart

AWS Machine Learning Blog

APRIL 7, 2025

Virginia) AWS Region. Prerequisites To try the Llama 4 models in SageMaker JumpStart, you need the following prerequisites: An AWS account that will contain all your AWS resources. An AWS Identity and Access Management (IAM) role to access SageMaker AI. b64encode(img).decode('utf-8') b64encode(response.content).decode('utf-8')

AWS

AWS Machine Learning Machine Learning ML

AWS Athena and Glue a Powerful Combo?

Towards AI

APRIL 3, 2024

Photo by Caspar Camille Rubin on Unsplash AWS Athena is a serverless interactive query system. The ORC and Parquet are columnal storage and they are famous in the Big Data world because of their efficient storage. First things first, load the sample data into the S3 bucket. Go to the AWS Glue Console. That is it!!

AWS

AWS Database ETL Big Data

Use Snowflake as a data source to train ML models with Amazon SageMaker

AWS Machine Learning Blog

MARCH 8, 2023

In such situations, it may be desirable to have the data accessible to SageMaker in the ephemeral storage media attached to the ephemeral training instances without the intermediate storage of data in Amazon S3. We add this data to Snowflake as a new table. Store your Snowflake account credentials in AWS Secrets Manager.

ML

ML ML AWS Python

An integrated experience for all your data and AI with Amazon SageMaker Unified Studio (preview)

Flipboard

DECEMBER 11, 2024

SageMaker Unied Studio is an integrated development environment (IDE) for data, analytics, and AI. Discover your data and put it to work using familiar AWS tools to complete end-to-end development workflows, including data analysis, data processing, model training, generative AI app building, and more, in a single governed environment.

SQL

SQL AWS Data Lakes AI

Building the future of construction analytics: CONXAI’s AI inference on Amazon EKS

AWS Machine Learning Blog

FEBRUARY 7, 2025

In this post, we dive deep into how CONXAI hosts the state-of-the-art OneFormer segmentation model on AWS using Amazon Simple Storage Service (Amazon S3), Amazon Elastic Kubernetes Service (Amazon EKS), KServe, and NVIDIA Triton. Our journey to AWS Initially, CONXAI started with a small cloud provider specializing in offering affordable GPUs.

Analytics

Analytics Analytics AWS Clustering

Revolutionizing clinical trials with the power of voice and AI

AWS Machine Learning Blog

MARCH 18, 2025

Solution overview: patient reporting and analysis in clinical trials Key AWS services used in this solution include Amazon Simple Storage Service (Amazon S3), AWS HealthScribe , Amazon Transcribe , and Amazon Bedrock. An AWS account. If you dont have one, you can register for a new AWS account.

AWS

AWS AI AI Data Quality

Learn the Difference between Big Data and Cloud Computing

Pickl AI

MARCH 11, 2025

Summary: Big Data and Cloud Computing are essential for modern businesses. Big Data analyses massive datasets for insights, while Cloud Computing provides scalable storage and computing power. Thats where big data and cloud computing come in. This massive collection of data is what we call Big Data.

Cloud Computing

Cloud Computing Big Data Big Data Big Data Analytics

Build a robust text-to-SQL solution generating complex queries, self-correcting, and querying diverse data sources

AWS Machine Learning Blog

FEBRUARY 28, 2024

To learn more about text-to-SQL best practices and design patterns, see Generating value from enterprise data: Best practices for Text2SQL and generative AI. Our solution aims to address those challenges using Amazon Bedrock and AWS Analytics Services. Install the AWS Command Line Interface (AWS CLI). format('parquet').option('path',

SQL

SQL AWS Database ML

Intelligently search Adobe Experience Manager content using Amazon Kendra

AWS Machine Learning Blog

SEPTEMBER 6, 2023

The Amazon Kendra AEM connector can integrate with AWS IAM Identity Center (Successor to AWS Single Sign-On). Prerequisites To try out the Amazon Kendra connector for AEM using this post as a reference, you need the following: An AWS account with privileges to create AWS Identity and Access Management (IAM) roles and policies.

AWS

AWS Analytics Analytics Cloud Computing

Governing the ML lifecycle at scale: Centralized observability with Amazon SageMaker and Amazon CloudWatch

AWS Machine Learning Blog

OCTOBER 29, 2024

You can use cross-account observability in CloudWatch to search, analyze, and correlate cross-account telemetry data stored in CloudWatch such as metrics, logs, and traces from one centralized account. You can now set up a central observability AWS account and connect your other accounts as sources.

ML

ML ML AWS Machine Learning

Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning Blog

NOVEMBER 9, 2023

Central model registry – Amazon SageMaker Model Registry is set up in a separate AWS account to track model versions generated across the dev and prod environments. with administrative privileges installed on AWS Terraform version 1.5.5 After the key is provisioned, it should be visible on the AWS KMS console.

AWS

AWS ML ML Machine Learning

Model management for LoRA fine-tuned models using Llama2 and Amazon SageMaker

AWS Machine Learning Blog

NOVEMBER 14, 2023

In the era of big data and AI, companies are continually seeking ways to use these technologies to gain a competitive edge. At the core of these cutting-edge solutions lies a foundation model (FM), a highly advanced machine learning model that is pre-trained on vast amounts of data. aws s3 cp {base_model_package.model_data}. !tar

ML

ML ML AWS SQL

Publish predictive dashboards in Amazon QuickSight using ML predictions from Amazon SageMaker Canvas

AWS Machine Learning Blog

MAY 10, 2023

In this post, we show how you can publish predictive dashboards in QuickSight using ML-based predictions from Canvas, without explicitly downloading predictions and importing into QuickSight. You can copy the prediction by choosing Copy , or download it by choosing Download prediction.

ML

ML ML AWS Data Analysis

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

In the following sections, we demonstrate how to import and prepare the data, optionally export the data, create a model, and run inference, all in SageMaker Canvas. Download the dataset from Kaggle and upload it to an Amazon Simple Storage Service (Amazon S3) bucket. Solutions Architect at AWS.

ML

ML ML Data Preparation AWS

Prepare image data with Amazon SageMaker Data Wrangler

Flipboard

MAY 1, 2023

You can also choose to onboard using AWS IAM Identity Center (successor to AWS Single Sign-On) for authentication (see Onboard to Amazon SageMaker Domain Using IAM Identity Center ). For this use case, we use CCTV footage data of accidents and non-accidents available from Kaggle.

Data Preparation

Data Preparation AWS ML ML

Accelerate data preparation for ML in Amazon SageMaker Canvas

AWS Machine Learning Blog

NOVEMBER 29, 2023

You can import data directly through over 50 data connectors such as Amazon Simple Storage Service (Amazon S3), Amazon Athena , Amazon Redshift , Snowflake, and Salesforce. In this walkthrough, we will cover importing your data directly from Snowflake. You can download the dataset loans-part-1.csv Product Manager at AWS.

Data Preparation

Data Preparation ML ML Data Quality

Prioritizing employee well-being: An innovative approach with generative AI and Amazon SageMaker Canvas

AWS Machine Learning Blog

JUNE 3, 2024

based single sign-on (SSO) methods, such as AWS IAM Identity Center. To learn more, see Secure access to Amazon SageMaker Studio with AWS SSO and a SAML application. To learn more about importing data to SageMaker Canvas, see Import data into Canvas. Choose Import data , then choose Tabular.

AWS

AWS ML ML AI

Access Snowflake data using OAuth-based authentication in Amazon SageMaker Data Wrangler

Flipboard

MARCH 22, 2023

In this post, we show how to configure a new OAuth-based authentication feature for using Snowflake in Amazon SageMaker Data Wrangler. Snowflake is a cloud data platform that provides data solutions for data warehousing to data science. For more information about prerequisites, see Get Started with Data Wrangler.

AWS

AWS Data Preparation Azure Data Scientist

Automate caption creation and search for images at enterprise scale using generative AI and Amazon Kendra

AWS Machine Learning Blog

AUGUST 2, 2023

We demonstrate CDE using simple examples and provide a step-by-step guide for you to experience CDE in an Amazon Kendra index in your own AWS account. Vit is a foundational model for image data, and GPT-2 is a foundational model for language. This means you can manipulate and ingest your data as needed.

AWS

AWS AI AI Machine Learning

Accelerate foundation model training and inference with Amazon SageMaker HyperPod and Amazon SageMaker Studio

AWS Machine Learning Blog

JUNE 19, 2025

Foundation Models (FMs) demand distributed training clusters — coordinated groups of accelerated compute instances , using frameworks like PyTorch — to parallelize workloads across hundreds of accelerators (like AWS Trainium and AWS Inferentia chips or NVIDIA GPUs). Figure 11: You can start developing locally!

Clustering

Clustering Data Scientist AWS ML

Schedule Amazon SageMaker notebook jobs and manage multi-step notebook workflows using APIs

AWS Machine Learning Blog

NOVEMBER 29, 2023

Each step of the workflow is developed in a different notebook, which are then converted into independent notebook jobs steps and connected as a pipeline: Preprocessing – Download the public SST2 dataset from Amazon Simple Storage Service (Amazon S3) and create a CSV file for the notebook in Step 2 to run. train sst2.train train sst2.train

ML

ML ML Data Scientist Python

Four approaches to manage Python packages in Amazon SageMaker Studio notebooks

Flipboard

MARCH 7, 2023

You can manage app images via the SageMaker console, the AWS SDK for Python (Boto3), and the AWS Command Line Interface (AWS CLI). The Studio Image Build CLI lets you build SageMaker-compatible Docker images directly from your Studio environments by using AWS CodeBuild. Environments without internet access.

Python

Python AWS ML ML

How To Use Oracle GoldenGate to Ingest Data Into Snowflake

phData

MARCH 7, 2023

From this stage, GoldenGate runs a merge statement to replicate data into Snowflake. Once an extract and distribution path is configured, follow these steps to ingest data into Snowflake. This will complete the installation of GoldenGate for Big Data. Download the Snowflake-JDBC Driver JAR File That can be done here.

Hadoop

Hadoop Database Data Warehouse AWS

Detect anomalies in manufacturing data using Amazon SageMaker Canvas

AWS Machine Learning Blog

FEBRUARY 15, 2024

With the use of cloud computing, big data and machine learning (ML) tools like Amazon Athena or Amazon SageMaker have become available and useable by anyone without much effort in creation and maintenance. This code typically runs inside an AWS Lambda function. For details, refer to Import data into Canvas.

ML

ML ML Data Science AWS

WiBD & DataCamp May Session – DataCamp Certification and Next Steps

Women in Big Data

MAY 21, 2024

We provided a quick overview of Women in Big Data (WiBD). Launched in 2015 and becoming a nonprofit organization in 2020, WiBD is a grassroots initiative dedicated to inspiring, connecting, and advancing women in data fields. Reviewed the Data Engineer Associate certification requirements and downloaded the study guide.

Data Engineering

Data Engineering Data Engineer Data Engineering Data Engineering

Cloud VPN Technology Makes Accessing Sports Content Easier

Smart Data Collective

FEBRUARY 10, 2022

Netflix uses AWS cloud services for optimizing almost all of its services. The cloud services provided through AWS help with everything from video transcribing, analytics, data storage and much more. We have talked about the benefits big data has brought to VPN technology.

AWS

AWS Big Data Big Data Analytics

Present and future of data cubes: an European EO perspective

Mlearning.ai

JANUARY 26, 2023

Most important to note about ARCO is that, unlike data systems from a decade ago, modern data cubes should ideally be Cloud-native (meaning: ready for fast and efficient web-services / scalable applications / API’s) and pre-processed so that they can be directly used for modelling and eventually for decision-making. Data, 4(3), 92.

AWS

AWS Database Data Science Clean Data

Enrich your AWS Glue Data Catalog with generative AI metadata using Amazon Bedrock

Flipboard

NOVEMBER 15, 2024

By harnessing the capabilities of generative AI, you can automate the generation of comprehensive metadata descriptions for your data assets based on their documentation, enhancing discoverability, understanding, and the overall data governance within your AWS Cloud environment. Python and boto3.

AWS

AWS Database AI AI

MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

JUNE 27, 2023

For example, if you use AWS, you may prefer Amazon SageMaker as an MLOps platform that integrates with other AWS services. SageMaker Studio offers built-in algorithms, automated model tuning, and seamless integration with AWS services, making it a powerful platform for developing and deploying machine learning solutions at scale.

Machine Learning

Machine Learning Machine Learning ML ML

How to manage an end-to-end machine learning project with MLflow? part 1

Mlearning.ai

FEBRUARY 17, 2023

(from the MLflow official website) There are over 13 million monthly downloads!! It works with many platforms such as Databrick, Azure, and AWS. It can be scaled to big data. It is easy to collaborate within a team (small or big). MLflow has become the standard platform for end-to-end MLOps.

Machine Learning

Machine Learning Machine Learning Python Database

How to Manage Unstructured Data in AI and Machine Learning Projects

DagsHub

OCTOBER 23, 2024

Data Lakes Data lakes are centralized repositories designed to store vast amounts of raw, unstructured, and structured data in their native format. They enable flexible data storage and retrieval for diverse use cases, making them highly scalable for big data applications.

Machine Learning

Machine Learning Machine Learning Data Lakes AI

Comparing Tools For Data Processing Pipelines

The MLOps Blog

MARCH 15, 2023

Some of the popular cloud-based vendors are: Hevo Data Equalum AWS DMS On the other hand, there are vendors offering on-premise data pipeline solutions and are mostly preferred by organizations dealing with highly sensitive data. No built-in data quality functionality. No expert support.

Data Pipeline

Data Pipeline ETL Data Quality SQL

Simplify data access for your enterprise using Amazon SageMaker Lakehouse

Flipboard

DECEMBER 4, 2024

Today, Example Retail Corp manages sales data in its data warehouse and customer data in Apache Iceberg tables in Amazon Simple Storage Service (Amazon S3). It uses Amazon EMR Serverless for data processing and machine learning. The Data Warehouse Admin has an IAM admin role and manages databases in Amazon Redshift.

Data Lakes

Data Lakes Data Warehouse AWS Database

Super charge your LLMs with RAG at scale using AWS Glue for Apache Spark

AWS Machine Learning Blog

OCTOBER 24, 2024

Diverse data amplifies the need for customizable cleaning and transformation logic to handle the quirks of different sources. In this post, we will explore building a reusable RAG data pipeline on LangChain —an open source framework for building applications based on LLMs—and integrating it with AWS Glue and Amazon OpenSearch Serverless.

AWS

AWS Data Pipeline Database Big Data

Automate Amazon EKS troubleshooting using an Amazon Bedrock agentic workflow

AWS Machine Learning Blog

APRIL 16, 2025

At AWS re:Invent 2024, we announced the multi-agent collaboration capability for Amazon Bedrock (preview). Prerequisites You need to have the following prerequisites in place: The AWS Command Line Interface (AWS CLI) version 2. For installation instructions, refer to Installing or updating to the latest version of the AWS CLI.

Clustering

Clustering AWS AI AI

Build and deploy a UI for your generative AI applications with AWS and Python

Search enterprise data assets using LLMs backed by knowledge graphs

Trending Sources

Fast and cost-effective LLaMA 2 fine-tuning with AWS Trainium

Unify structured data in Amazon Aurora and unstructured data in Amazon S3 for insights using Amazon Q

Harmonize data using AWS Glue and AWS Lake Formation FindMatches ML to build a customer 360 view

Frugality meets Accuracy: Cost-efficient training of GPT NeoX and Pythia models with AWS Trainium

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

NeMo Retriever Llama 3.2 text embedding and reranking NVIDIA NIM microservices now available in Amazon SageMaker JumpStart

Llama 4 family of models from Meta are now available in SageMaker JumpStart

AWS Athena and Glue a Powerful Combo?

Use Snowflake as a data source to train ML models with Amazon SageMaker

An integrated experience for all your data and AI with Amazon SageMaker Unified Studio (preview)

Building the future of construction analytics: CONXAI’s AI inference on Amazon EKS

Revolutionizing clinical trials with the power of voice and AI

Learn the Difference between Big Data and Cloud Computing

Build a robust text-to-SQL solution generating complex queries, self-correcting, and querying diverse data sources

Intelligently search Adobe Experience Manager content using Amazon Kendra

Governing the ML lifecycle at scale: Centralized observability with Amazon SageMaker and Amazon CloudWatch

Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

Model management for LoRA fine-tuned models using Llama2 and Amazon SageMaker

Publish predictive dashboards in Amazon QuickSight using ML predictions from Amazon SageMaker Canvas

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

Prepare image data with Amazon SageMaker Data Wrangler

Accelerate data preparation for ML in Amazon SageMaker Canvas

Prioritizing employee well-being: An innovative approach with generative AI and Amazon SageMaker Canvas

Access Snowflake data using OAuth-based authentication in Amazon SageMaker Data Wrangler

Automate caption creation and search for images at enterprise scale using generative AI and Amazon Kendra

Accelerate foundation model training and inference with Amazon SageMaker HyperPod and Amazon SageMaker Studio

Schedule Amazon SageMaker notebook jobs and manage multi-step notebook workflows using APIs

Four approaches to manage Python packages in Amazon SageMaker Studio notebooks

How To Use Oracle GoldenGate to Ingest Data Into Snowflake

Detect anomalies in manufacturing data using Amazon SageMaker Canvas

WiBD & DataCamp May Session – DataCamp Certification and Next Steps

Cloud VPN Technology Makes Accessing Sports Content Easier

Present and future of data cubes: an European EO perspective

Enrich your AWS Glue Data Catalog with generative AI metadata using Amazon Bedrock

MLOps Landscape in 2023: Top Tools and Platforms

How to manage an end-to-end machine learning project with MLflow? part 1

How to Manage Unstructured Data in AI and Machine Learning Projects

Comparing Tools For Data Processing Pipelines

Simplify data access for your enterprise using Amazon SageMaker Lakehouse

Super charge your LLMs with RAG at scale using AWS Glue for Apache Spark

Automate Amazon EKS troubleshooting using an Amazon Bedrock agentic workflow

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