AWS Machine Learning Blog

JULY 17, 2024

As a customer, you rely on Amazon Web Services (AWS) expertise to be available and understand your specific environment and operations. Amazon Q Business is a fully managed, secure, generative-AI powered enterprise chat assistant that enables natural language interactions with your organization’s data.

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Data Science Blog

JULY 23, 2023

Data Mesh on Azure Cloud with Databricks and Delta Lake for Applications of Business Intelligence, Data Science and Process Mining. However, this concept on the Azure Cloud is just an example and can easily be implemented on the Google Cloud (GCP), Amazon Cloud (AWS) and now even on the SAP Cloud (Datasphere) using Databricks.

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AWS Machine Learning Blog

JANUARY 24, 2025

In this post, we delve into the essential security best practices that organizations should consider when fine-tuning generative AI models. Security in Amazon Bedrock Cloud security at AWS is the highest priority. Amazon Bedrock prioritizes security through a comprehensive approach to protect customer data and AI workloads.

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AWS Machine Learning Blog

JANUARY 5, 2024

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models.

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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.

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AWS Machine Learning Blog

APRIL 29, 2024

In 2024, however, organizations are using large language models (LLMs), which require relatively little focus on NLP, shifting research and development from modeling to the infrastructure needed to support LLM workflows. This often means the method of using a third-party LLM API won’t do for security, control, and scale reasons.

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Flipboard

NOVEMBER 20, 2024

By combining the capabilities of LLM function calling and Pydantic data models, you can dynamically extract metadata from user queries. Prerequisites Before proceeding with this tutorial, make sure you have the following in place: AWS account – You should have an AWS account with access to Amazon Bedrock.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

In addition to its groundbreaking AI innovations, Zeta Global has harnessed Amazon Elastic Container Service (Amazon ECS) with AWS Fargate to deploy a multitude of smaller models efficiently. Additionally, Feast promotes feature reuse, so the time spent on data preparation is reduced greatly.

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AWS Machine Learning Blog

JANUARY 27, 2025

For this post, we use the us-east-1 AWS Region: Have access to a POSIX based (Mac/Linux) system or SageMaker notebooks. Both MMCV and Prithvi are third-party models which have not undergone AWS security reviews, so please review these models yourself or use at your own risk.

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AWS Machine Learning Blog

NOVEMBER 21, 2024

We guide you through deploying the necessary infrastructure using AWS CloudFormation , creating an internal labeling workforce, and setting up your first labeling job. This precision helps models learn the fine details that separate natural from artificial-sounding speech. We demonstrate how to use Wavesurfer.js

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AWS Machine Learning Blog

SEPTEMBER 5, 2023

The solution framework is scalable as more equipment is installed and can be reused for a variety of downstream modeling tasks. In this post, we show how the Carrier and AWS teams applied ML to predict faults across large fleets of equipment using a single model. The effective precision of the trained model is 91.6%.

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AWS Machine Learning Blog

NOVEMBER 13, 2024

Because SageMaker Model Cards and SageMaker Model Registry were built on separate APIs, it was challenging to associate the model information and gain a comprehensive view of the model development lifecycle. Integrating model information and then sharing it across different stages became increasingly difficult.

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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 (..)

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AWS Machine Learning Blog

NOVEMBER 22, 2024

Using this approach, you can focus on developing and refining the model while using the fully managed training infrastructure provided by SageMaker Training. Implementation details We spin up the cluster by calling the SageMaker control plane through APIs or the AWS Command Line Interface (AWS CLI) or using the SageMaker AWS SDK.

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Data Science Connect

JANUARY 27, 2023

Understanding how data warehousing works and how to design and implement a data warehouse is an important skill for a data engineer. Learn about data modeling: Data modeling is the process of creating a conceptual representation of data.

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Data Science Dojo

OCTOBER 31, 2024

Key Skills Proficiency in SQL is essential, along with experience in data visualization tools such as Tableau or Power BI. Strong analytical skills and the ability to work with large datasets are critical, as is familiarity with data modeling and ETL processes.

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AWS Machine Learning Blog

APRIL 22, 2025

Haiku while still maintaining the baseline accuracy with the larger/ more expensive model (Sonnet 3.5 Getting started You can get started using the AWS Management Console for Amazon Bedrock. You can also use AWS Command Line Interface (AWS CLI) or API, to configure and use a prompt router.

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phData

SEPTEMBER 19, 2023

However, to fully harness the potential of a data lake, effective data modeling methodologies and processes are crucial. Data modeling plays a pivotal role in defining the structure, relationships, and semantics of data within a data lake. Consistency of data throughout the data lake.

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AWS Machine Learning Blog

JUNE 22, 2023

Working with AWS, Light & Wonder recently developed an industry-first secure solution, Light & Wonder Connect (LnW Connect), to stream telemetry and machine health data from roughly half a million electronic gaming machines distributed across its casino customer base globally when LnW Connect reaches its full potential.

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AWS Machine Learning Blog

MARCH 7, 2025

Today, Amazon Web Services (AWS) announced the general availability of Amazon Bedrock Knowledge Bases GraphRAG (GraphRAG), a capability in Amazon Bedrock Knowledge Bases that enhances Retrieval-Augmented Generation (RAG) with graph data in Amazon Neptune Analytics. For Data source details , select Amazon S3 as your data source.

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Data Science Blog

SEPTEMBER 19, 2023

This ensures that the data models and queries developed by data professionals are consistent with the underlying infrastructure. Enhanced Security and Compliance Data Warehouses often store sensitive information, making security a paramount concern. IaC allows these teams to collaborate more effectively.

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AWS Machine Learning Blog

AUGUST 21, 2023

You can streamline the process of feature engineering and data preparation with SageMaker Data Wrangler and finish each stage of the data preparation workflow (including data selection, purification, exploration, visualization, and processing at scale) within a single visual interface.

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AWS Machine Learning Blog

NOVEMBER 18, 2024

Secure model access – Secure, private model access using AWS PrivateLink gives controlled data transfer for inference without traversing the public internet, maintaining data privacy and helping to adhere to compliance requirements.

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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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AWS Machine Learning Blog

OCTOBER 20, 2023

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries. The architecture maps the different capabilities of the ML platform to AWS accounts.

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Smart Data Collective

OCTOBER 19, 2022

You can only deploy DynamoDB on Amazon Web Services (AWS), and it does not support on-premise deployments. With DynamoDB, you are essentially locked into AWS as your cloud provider. MongoDB is deployable anywhere, and the MongoDB Atlas database-as-a-service can be deployed on AWS, Azure, and Google Cloud Platform (GCP).

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Data Science Dojo

JULY 6, 2023

Amazon Redshift: Amazon Redshift is a cloud-based data warehousing service provided by Amazon Web Services (AWS). Amazon Redshift allows data engineers to analyze large datasets quickly using massively parallel processing (MPP) architecture. It is known for its high performance and cost-effectiveness.

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AWS Machine Learning Blog

MAY 31, 2024

With the Amazon Bedrock serverless experience, you can get started quickly, privately customize FMs with your own data, and integrate and deploy them into your applications using the Amazon Web Services (AWS) tools without having to manage infrastructure. However, this is beyond the scope of this post.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

The AWS Well-Architected Framework provides a systematic way for organizations to learn operational and architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable workloads in the cloud. These resources introduce common AWS services for IDP workloads and suggested workflows.

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AWS Machine Learning Blog

APRIL 25, 2024

We provide a comprehensive guide on how to deploy speaker segmentation and clustering solutions using SageMaker on the AWS Cloud. Solution overview Amazon Transcribe is the go-to service for speaker diarization in AWS. Hugging Face is a popular open source hub for machine learning (ML) models.

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AWS Machine Learning Blog

MAY 4, 2023

Forecast uses ML to learn not only the best algorithm for each item, but also the best ensemble of algorithms for each item, automatically creating the best model for your data. The console and AWS CLI methods are best suited for quick experimentation to check the feasibility of time series forecasting using your data.

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AWS Machine Learning Blog

FEBRUARY 27, 2024

In this post, we share how Axfood, a large Swedish food retailer, improved operations and scalability of their existing artificial intelligence (AI) and machine learning (ML) operations by prototyping in close collaboration with AWS experts and using Amazon SageMaker.

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AWS Machine Learning Blog

SEPTEMBER 19, 2024

In this post, AWS collaborates with Meta’s PyTorch team to showcase how you can use Meta’s torchtune library to fine-tune Meta Llama-like architectures while using a fully-managed environment provided by Amazon SageMaker Training. cat config_l3.1_8b_lora.yaml # Model Arguments model: _component_: torchtune.models.llama3_1.lora_llama3_1_8b

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Dataconomy

SEPTEMBER 4, 2023

Key features of cloud analytics solutions include: Data models , Processing applications, and Analytics models. Data models help visualize and organize data, processing applications handle large datasets efficiently, and analytics models aid in understanding complex data sets, laying the foundation for business intelligence.

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Mlearning.ai

JUNE 7, 2023

AWS Inferentia accelerators are custom-built machine learning inference chips designed by Amazon Web Services (AWS) to optimize inference workloads on the AWS platform. The AWS Inferentia chips are designed with a focus on delivering high performance, low latency, and cost efficiency for inference workloads.

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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. This includes Meta Llama 3, Meta’s publicly available large language model (LLM).

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Towards AI

APRIL 28, 2025

Volgas On-Demand Layer was deployed behind an AWS Application Load Balancer, serving as the primary target for Locust workers and routing requests to the EKS nodes hosting Volga pods (where the OS then distributed the load among workers within the same node/pod). Tests setup We ran load tests on an Amazon EKS cluster using t2.medium

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