AWS Machine Learning Blog

OCTOBER 16, 2024

Amazon SageMaker supports geospatial machine learning (ML) capabilities, allowing data scientists and ML engineers to build, train, and deploy ML models using geospatial data. SageMaker Processing provisions cluster resources for you to run city-, country-, or continent-scale geospatial ML workloads.

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AWS Machine Learning Blog

OCTOBER 28, 2024

This fragmentation can complicate efforts by organizations to consolidate and analyze data for their machine learning (ML) initiatives. This minimizes the complexity and overhead associated with moving data between cloud environments, enabling organizations to access and utilize their disparate data assets for ML projects.

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PyImageSearch

DECEMBER 30, 2024

Jump Right To The Downloads Section Need Help Configuring Your Development Environment? Hugging Face Spaces is a platform for deploying and sharing machine learning (ML) applications with the community. It offers an interactive interface, enabling users to explore ML models directly in their browser without the need for local setup.

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AWS Machine Learning Blog

DECEMBER 2, 2024

Challenges in deploying LLMs for inference As LLMs and their respective hosting containers continue to grow in size and complexity, AI and ML engineers face increasing challenges in deploying and scaling these models efficiently for inference. Weight streaming Fast Model Loader streams weights directly from Amazon S3 to GPUs.

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AWS Machine Learning Blog

SEPTEMBER 20, 2023

In these scenarios, as you start to embrace generative AI, large language models (LLMs) and machine learning (ML) technologies as a core part of your business, you may be looking for options to take advantage of AWS AI and ML capabilities outside of AWS in a multicloud environment.

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AWS Machine Learning Blog

OCTOBER 24, 2024

Machine learning (ML) helps organizations to increase revenue, drive business growth, and reduce costs by optimizing core business functions such as supply and demand forecasting, customer churn prediction, credit risk scoring, pricing, predicting late shipments, and many others. You can now view the predictions and download them as CSV.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

Data preparation is a crucial step in any machine learning (ML) workflow, yet it often involves tedious and time-consuming tasks. With this integration, SageMaker Canvas provides customers with an end-to-end no-code workspace to prepare data, build and use ML and foundations models to accelerate time from data to business insights.

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Flipboard

DECEMBER 2, 2024

This long-awaited capability is a game changer for our customers using the power of AI and machine learning (ML) inference in the cloud. The scale down to zero feature presents new opportunities for how businesses can approach their cloud-based ML operations. However, it’s possible to forget to delete these endpoints when you’re done.

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AWS Machine Learning Blog

MAY 16, 2025

Are you looking to deploy machine learning (ML) models at the edge? With Amazon SageMaker AI and SiMa.ais Palette Edgematic platform, you can efficiently build, train, and deploy optimized ML models at the edge for a variety of use cases. Edgematic with SageMaker JupyterLab to deploy an ML model, YOLOv7 , to the edge.

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AWS Machine Learning Blog

DECEMBER 2, 2024

Container Caching addresses this scaling challenge by pre-caching the container image, eliminating the need to download it when scaling up. We discuss how this innovation significantly reduces container download and load times during scaling events, a major bottleneck in LLM and generative AI inference.

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AWS Machine Learning Blog

FEBRUARY 11, 2025

GraphStorm is a low-code enterprise graph machine learning (ML) framework that provides ML practitioners a simple way of building, training, and deploying graph ML solutions on industry-scale graph data. To download and preprocess the data as an Amazon SageMaker Processing step, use the following code. million edges.

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AWS Machine Learning Blog

DECEMBER 16, 2024

Getting started with SageMaker JumpStart SageMaker JumpStart is a machine learning (ML) hub that can help accelerate your ML journey. This feature eliminates one of the major bottlenecks in deployment scaling by pre-caching container images, removing the need for time-consuming downloads when adding new instances.

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AWS Machine Learning Blog

OCTOBER 18, 2024

Business challenge Today, many developers use AI and machine learning (ML) models to tackle a variety of business cases, from smart identification and natural language processing (NLP) to AI assistants. This example uses the download_tar_and_untar utility to download the model to a local drive.

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AWS Machine Learning Blog

OCTOBER 29, 2024

This post is part of an ongoing series on governing the machine learning (ML) lifecycle at scale. To start from the beginning, refer to Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker. We use SageMaker Model Monitor to assess these models’ performance.

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AWS Machine Learning Blog

FEBRUARY 19, 2024

A SageMaker MME dynamically loads models from Amazon Simple Storage Service (Amazon S3) when invoked, instead of downloading all the models when the endpoint is first created. If the model is already loaded on the container when invoked, then the download step is skipped and the model returns the inferences with low latency.

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Flipboard

FEBRUARY 11, 2025

By using Amazon Q Business, which simplifies the complexity of developing and managing ML infrastructure and models, the team rapidly deployed their chat solution. With a deep passion for driving performance improvements, he dedicates himself to empowering both customers and teams through innovative ML-enabled solutions.

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AWS Machine Learning Blog

JANUARY 29, 2025

Yanyan Zhang is a Senior Generative AI Data Scientist at Amazon Web Services, where she has been working on cutting-edge AI/ML technologies as a Generative AI Specialist, helping customers use generative AI to achieve their desired outcomes. Yanyan graduated from Texas A&M University with a PhD in Electrical Engineering.

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Flipboard

JUNE 5, 2025

Amazon SageMaker AI provides a fully managed service for deploying these machine learning (ML) models with multiple inference options, allowing organizations to optimize for cost, latency, and throughput. AWS has always provided customers with choice. That includes model choice, hardware choice, and tooling choice.

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AWS Machine Learning Blog

NOVEMBER 13, 2024

To upload the dataset Download the dataset : Go to the Shoe Dataset page on Kaggle.com and download the dataset file (350.79MB) that contains the images. To do so, find the best extracted image in the local directory created when the images were downloaded. b64encode(image_file.read()).decode('utf-8')

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AWS Machine Learning Blog

JANUARY 31, 2025

script that automatically downloads and organizes the data in your EFS storage. The Lizard dataset is available on Kaggle , and our repository includes scripts to automatically download and prepare the data for training. Our repository includes a download_mhist.sh Wed love to hear about your experiences and insights.

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ML @ CMU

APRIL 9, 2025

Thus far, over 11,000 users have downloaded Copilot Arena, and the tool has served over 100K completions, and accumulated over 25,000 code completion battles. In contrast, Copilot Arena users are working on a diverse set of realistic tasks, including but not limited to frontend components, backend logic, and ML pipelines.

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AWS Machine Learning Blog

DECEMBER 24, 2024

source env_vars After setting your environment variables, download the lifecycle scripts required for bootstrapping the compute nodes on your SageMaker HyperPod cluster and define its configuration settings before uploading the scripts to your S3 bucket. script to download the model and tokenizer. architectures/5.sagemaker-hyperpod/LifecycleScripts/base-config/

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AWS Machine Learning Blog

AUGUST 15, 2024

With over 50 connectors, an intuitive Chat for data prep interface, and petabyte support, SageMaker Canvas provides a scalable, low-code/no-code (LCNC) ML solution for handling real-world, enterprise use cases. Afterward, you need to manage complex clusters to process and train your ML models over these large-scale datasets.

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Flipboard

APRIL 23, 2025

When processing is triggered, endpoints are automatically initialized and model artifacts are downloaded from Amazon S3. In addition, he builds and deploys AI/ML models on the AWS Cloud. Additionally, Ian focuses on building AI/ML solutions using AWS services. The LLM endpoint is provisioned on ml.p4d.24xlarge

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AWS Machine Learning Blog

JANUARY 21, 2025

In this post, we show you how Amazon Web Services (AWS) helps in solving forecasting challenges by customizing machine learning (ML) models for forecasting. This visual, point-and-click interface democratizes ML so users can take advantage of the power of AI for various business applications. To download a copy of this dataset, visit.

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Data Science Dojo

SEPTEMBER 10, 2024

LLM companies are businesses that specialize in developing and deploying Large Language Models (LLMs) and advanced machine learning (ML) models. WhyLabs WhyLabs is renowned for its versatile and robust machine learning (ML) observability platform. million downloads, demonstrating its widespread adoption and effectiveness.

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Flipboard

FEBRUARY 10, 2025

Whether youre new to Gradio or looking to expand your machine learning (ML) toolkit, this guide will equip you to create versatile and impactful applications. Jump Right To The Downloads Section What Is Gradio and Why Is It Ideal for Chatbots? Model Management: Easily download, run, and manage various models, including Llama 3.2

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AWS Machine Learning Blog

DECEMBER 4, 2024

Building generative AI applications presents significant challenges for organizations: they require specialized ML expertise, complex infrastructure management, and careful orchestration of multiple services. Download all three sample data files. Import the API schema from the openapi_schema.json file that you downloaded earlier.

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AWS Machine Learning Blog

MARCH 13, 2025

HF_TOKEN : This parameter variable provides the access token required to download gated models from the Hugging Face Hub, such as Llama or Mistral. Model Base Model Download DeepSeek-R1-Distill-Qwen-1.5B Model Base Model Download DeepSeek-R1-Distill-Qwen-1.5B meta-llama/Llama-3.2-11B-Vision-Instruct

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AWS Machine Learning Blog

MARCH 3, 2025

This design simplifies the complexity of distributed training while maintaining the flexibility needed for diverse machine learning (ML) workloads, making it an ideal solution for enterprise AI development. Download the prepared dataset that you uploaded to S3 into the FSx for Lustre volume attached to the cluster. instance_type: p4d.24xlarge.

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AWS Machine Learning Blog

MAY 14, 2025

Create model trainer model_trainer = create_model_trainer( compute=compute, network=network, data_channel=data_channel, action="download", source_code=source_code. Create model trainer model_trainer = create_model_trainer( compute=compute, network=network, data_channel=data_channel, action="download", source_code=source_code.

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AWS Machine Learning Blog

NOVEMBER 29, 2024

For example, marketing and software as a service (SaaS) companies can personalize artificial intelligence and machine learning (AI/ML) applications using each of their customer’s images, art style, communication style, and documents to create campaigns and artifacts that represent them. _region_name sm_client = boto3.client(service_name='sagemaker')

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Flipboard

NOVEMBER 27, 2024

He helps architect solutions across AI/ML applications, enterprise data platforms, data governance, and unified search in enterprises. Gi Kim is a Data & ML Engineer with the AWS Professional Services team, helping customers build data analytics solutions and AI/ML applications.

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AWS Machine Learning Blog

JUNE 5, 2025

In this post, we share how Radial optimized the cost and performance of their fraud detection machine learning (ML) applications by modernizing their ML workflow using Amazon SageMaker. Businesses need for fraud detection models ML has proven to be an effective approach in fraud detection compared to traditional approaches.

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AWS Machine Learning Blog

APRIL 3, 2024

Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. This persona typically is only a SageMaker Canvas user and often relies on ML experts in their organization to review and approve their work.

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AWS Machine Learning Blog

MARCH 11, 2025

Solution overview You can use DeepSeeks distilled models within the AWS managed machine learning (ML) infrastructure. This method is generally much faster, with the model typically downloading in just a couple of minutes from Amazon S3. Pranav Murthy is an AI/ML Specialist Solutions Architect at AWS.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

jpg", "prompt": "Which part of Virginia is this letter sent from", "completion": "Richmond"} SageMaker JumpStart SageMaker JumpStart is a powerful feature within the SageMaker machine learning (ML) environment that provides ML practitioners a comprehensive hub of publicly available and proprietary foundation models (FMs).

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