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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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. Using the Ollama API (this tutorial) To learn how to build a multimodal chatbot with Gradio, Llama 3.2, and the Ollama API, just keep reading. ollama/models directory.

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AWS Machine Learning Blog

DECEMBER 24, 2024

Trainium chips are purpose-built for deep learning training of 100 billion and larger parameter models. Model training on Trainium is supported by the AWS Neuron SDK, which provides compiler, runtime, and profiling tools that unlock high-performance and cost-effective deep learning acceleration. architectures/5.sagemaker-hyperpod/LifecycleScripts/base-config/

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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.

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PyImageSearch

JANUARY 13, 2025

To learn how to master YOLO11 and harness its capabilities for various computer vision tasks , just keep reading. Jump Right To The Downloads Section What Is YOLO11? VideoCapture(input_video_path) Next, we download the input video from the pyimagesearch/images-and-videos repository using the hf_hub_download() function.

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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.

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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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AWS Machine Learning Blog

JULY 3, 2023

It’s one of the prerequisite tasks to prepare training data to train a deep learning model. Specifically, for deep learning-based autonomous vehicle (AV) and Advanced Driver Assistance Systems (ADAS), there is a need to label complex multi-modal data from scratch, including synchronized LiDAR, RADAR, and multi-camera streams.

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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

JUNE 6, 2023

PyTorch is a machine learning (ML) framework that is widely used by AWS customers for a variety of applications, such as computer vision, natural language processing, content creation, and more. These are basically big models based on deep learning techniques that are trained with hundreds of billions of parameters.

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Flipboard

AUGUST 17, 2023

Many practitioners are extending these Redshift datasets at scale for machine learning (ML) using Amazon SageMaker , a fully managed ML service, with requirements to develop features offline in a code way or low-code/no-code way, store featured data from Amazon Redshift, and make this happen at scale in a production environment.

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AWS Machine Learning Blog

MAY 31, 2023

PyTorch is a machine learning (ML) framework based on the Torch library, used for applications such as computer vision and natural language processing. This provides a major flexibility advantage over the majority of ML frameworks, which require neural networks to be defined as static objects before runtime.

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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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AWS Machine Learning Blog

JUNE 9, 2023

ONNX ( Open Neural Network Exchange ) is an open-source standard for representing deep learning models widely supported by many providers. ONNX provides tools for optimizing and quantizing models to reduce the memory and compute needed to run machine learning (ML) models. For more details, refer to CreateEndpointConfig.

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IBM Data Science in Practice

NOVEMBER 21, 2022

Leverage the Watson NLP library to build the best classification models by combining the power of classic ML, Deep Learning, and Transformed based models. In this blog, you will walk through the steps of building several ML and Deep learning-based models using the Watson NLP library.

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PyImageSearch

MAY 19, 2025

Jump Right To The Downloads Section Introduction In the previous post , we walked through the process of indexing and storing movie data in OpenSearch. If you havent already set up the project from the previous post, you can download the source code from the tutorials “Downloads” section. data queries_set_1.txt

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AWS Machine Learning Blog

SEPTEMBER 23, 2024

Machine learning (ML) projects are inherently complex, involving multiple intricate steps—from data collection and preprocessing to model building, deployment, and maintenance. To start our ML project predicting the probability of readmission for diabetes patients, you need to download the Diabetes 130-US hospitals dataset.

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AWS Machine Learning Blog

JANUARY 27, 2025

Amazon SageMake r provides a seamless experience for building, training, and deploying machine learning (ML) models at scale. You use an AWS Deep Learning SageMaker framework container as the base image because it includes required dependencies such as SageMaker libraries, PyTorch, and CUDA. repeat(1, 1, pred.shape[-1])).detach().cpu()

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Flipboard

JUNE 26, 2023

These techniques utilize various machine learning (ML) based approaches. 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.

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PyImageSearch

DECEMBER 18, 2023

This format made for a fast-paced and diverse showcase of ideas and applications in AI and ML. In just 3 minutes, each participant managed to highlight the core of their work, offering insights into the innovative ways in which AI and ML are being applied across various fields. But again, stick around for a surprise demo at the end. ?

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AWS Machine Learning Blog

MAY 8, 2023

SageMaker provides single model endpoints (SMEs), which allow you to deploy a single ML model, or multi-model endpoints (MMEs), which allow you to specify multiple models to host behind a logical endpoint for higher resource utilization. TensorRT is an SDK developed by NVIDIA that provides a high-performance deep learning inference library.

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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

APRIL 19, 2023

The DJL is a deep learning framework built from the ground up to support users of Java and JVM languages like Scala, Kotlin, and Clojure. The DJL is a deep learning framework built from the ground up to support users of Java and JVM languages like Scala, Kotlin, and Clojure. We recently developed four more new models.

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AWS Machine Learning Blog

MAY 9, 2023

Amazon SageMaker provides a number of options for users who are looking for a solution to host their machine learning (ML) models. For that use case, SageMaker provides SageMaker single model endpoints (SMEs), which allow you to deploy a single ML model against a logical endpoint.

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Flipboard

FEBRUARY 7, 2023

Amazon SageMaker Studio Lab provides no-cost access to a machine learning (ML) development environment to everyone with an email address. The following example illustrates Studio Lab running a Jupyter notebook that downloads TCIA prostate MRI data, segments it using MONAI, and displays the results using itkWidgets.

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AWS Machine Learning Blog

APRIL 22, 2025

You can also download model from Amazon Simple Storage Service (Amazon S3). HF_TOKEN sets the token to download the model. To review the latest available container version, see Available Deep Learning Containers Images. Siddharth Venkatesan is a Software Engineer in AWS Deep Learning.

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AWS Machine Learning Blog

MAY 30, 2023

Since its introduction, we’ve helped hundreds of customers optimize their workloads, set guardrails, and improve the visibility of their machine learning (ML) workloads’ cost and usage. In this series of posts, we share lessons learned about optimizing costs in Amazon SageMaker. In this post, we focus on SageMaker training jobs.

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Flipboard

SEPTEMBER 4, 2023

This lesson is the 1st of a 3-part series on Docker for Machine Learning : Getting Started with Docker for Machine Learning (this tutorial) Lesson 2 Lesson 3 Overview: Why the Need? Envision yourself as an ML Engineer at one of the world’s largest companies. How Do Containers Differ from Virtual Machines? Follow along!

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Applied Data Science

AUGUST 2, 2021

Download the free, unabridged version here. They bring deep expertise in machine learning , clustering , natural language processing , time series modelling , optimisation , hypothesis testing and deep learning to the team. Give this technique a try to take your team’s ML modelling to the next level.

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Towards AI

JUNE 27, 2023

This list, encompassing the most effective machine learning tools, serves as a valuable guide for developers seeking to enhance their machine learning toolkit and drive the AI revolution forward. This open-source library is renowned for its capabilities in numerical computation, particularly in large-scale machine learning projects.

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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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AWS Machine Learning Blog

JANUARY 9, 2023

Machine learning (ML) applications are complex to deploy and often require the ability to hyper-scale, and have ultra-low latency requirements and stringent cost budgets. Deploying ML models at scale with optimized cost and compute efficiencies can be a daunting and cumbersome task. Design patterns for building ML applications.

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Flipboard

MAY 5, 2025

Jump Right To The Downloads Section Introduction What Is AWS OpenSearch? Semantic search improves accuracy by leveraging machine learning (ML), natural language processing (NLP), and vector search techniques to deliver more relevant, intent-driven results. Learning to Rank (LTR) and Re-Ranking: Uses ML models (e.g.,

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Data Science Dojo

JUNE 14, 2023

Learn how the synergy of AI and ML algorithms in paraphrasing tools is redefining communication through intelligent algorithms that enhance language expression. The most revolutionary technology that enables this is called machine learning. Paraphrasing tools in AI and ML algorithms Machine learning is a subset of AI.

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Data Science Dojo

JUNE 14, 2023

Learn how the synergy of AI and ML algorithms in paraphrasing tools is redefining communication through intelligent algorithms that enhance language expression. The most revolutionary technology that enables this is called machine learning. Paraphrasing tools in AI and ML algorithms Machine learning is a subset of AI.

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AWS Machine Learning Blog

MAY 1, 2024

AWS Trainium instances for training workloads SageMaker ml.trn1 and ml.trn1n instances, powered by Trainium accelerators, are purpose-built for high-performance deep learning training and offer up to 50% cost-to-train savings over comparable training optimized Amazon Elastic Compute Cloud (Amazon EC2) instances.

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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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