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

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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Data Science Blog

NOVEMBER 15, 2023

New big data architectures and, above all, data sharing concepts such as Data Mesh are ideal for creating a common database for many data products and applications. The Event Log Data Model for Process Mining Process Mining as an analytical system can very well be imagined as an iceberg.

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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

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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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

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

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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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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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

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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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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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

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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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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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

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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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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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

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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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

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

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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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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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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Mlearning.ai

DECEMBER 22, 2023

You can jump AWS authentication steps if you're already in AWS 's environment. Tip: Only include libraries with the most updated version in the requirements.txt file, specially the transformerslibrary as it may miss newly updated models in older versions. You can find here more about it. 1.46k [00:00<?

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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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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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AWS Machine Learning Blog

FEBRUARY 7, 2024

In the first post of this three-part series, we presented 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. Add rules to interpret model scores. It’s recommended to use at least 3–6 months of data.

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The MLOps Blog

JANUARY 11, 2023

This can enable the company to leverage the data generated by its IoT edge devices to drive business decisions and gain a competitive advantage. AWS offers a three-layered machine learning stack to choose from based on your skill set and team’s requirements for implementing workloads to execute machine learning tasks.

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AWS Machine Learning Blog

SEPTEMBER 12, 2024

In this post, we explore the journey that Thomson Reuters took to enable cutting-edge research in training domain-adapted large language models (LLMs) using Amazon SageMaker HyperPod , an Amazon Web Services (AWS) feature focused on providing purpose-built infrastructure for distributed training at scale.

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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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AWS Machine Learning Blog

JULY 29, 2024

Launched in August 2019, Forecast predates Amazon SageMaker Canvas , a popular low-code no-code AWS tool for building, customizing, and deploying ML models, including time series forecasting models. For more information about AWS Region availability, see AWS Services by Region.

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AWS Machine Learning Blog

JUNE 27, 2023

By enabling effective management of the ML lifecycle, MLOps can help account for various alterations in data, models, and concepts that the development of real-time image recognition applications is associated with. At-scale, real-time image recognition is a complex technical problem that also requires the implementation of MLOps.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

Prerequisites The following are prerequisites for completing the walkthrough in this post: An AWS account Familiarity with SageMaker concepts, such as an Estimator, training job, and HPO job Familiarity with the Amazon SageMaker Python SDK Python programming knowledge Implement the solution The full code is available in the GitHub repo.

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AWS Machine Learning Blog

JUNE 5, 2023

We use PEFT to optimize this model for the specific task of summarizing messenger-like conversations. The single-GPU instance that we use is a low-cost example of the many instance types AWS provides. Training this model on a single GPU highlights AWS’s commitment to being the most cost-effective provider of AI/ML services.

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O'Reilly Media

OCTOBER 19, 2021

We need robust versioning for data, models, code, and preferably even the internal state of applications—think Git on steroids to answer inevitable questions: What changed? ML use cases rarely dictate the master data management solution, so the ML stack needs to integrate with existing data warehouses.

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