AWS Machine Learning Blog

OCTOBER 24, 2024

Example code The following code example is a Python script that can be used as an AWS Lambda function or as part of your processing pipeline. Here’s a high-level breakdown of how the Python script is executed: Load the YOLOv9 model – This model is used for detecting objects in each frame. pip install opencv-python ultralytics !pip

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Flipboard

DECEMBER 2, 2024

We explore two approaches: using the SageMaker Python SDK for programmatic implementation, and using the Amazon SageMaker Studio UI for a more visual, interactive experience. In this post, we walked through the step-by-step process of implementing this feature through both the SageMaker Python SDK and SageMaker Studio UI.

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How to Learn Machine Learning

DECEMBER 24, 2024

If you’re diving into the world of machine learning, AWS Machine Learning provides a robust and accessible platform to turn your data science dreams into reality. Today, we’ll explore why Amazon’s cloud-based machine learning services could be your perfect starting point for building AI-powered applications.

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Data Science Dojo

OCTOBER 31, 2024

Key Skills: Mastery in machine learning frameworks like PyTorch or TensorFlow is essential, along with a solid foundation in unsupervised learning methods. Stanford AI Lab recommends proficiency in deep learning, especially if working in experimental or cutting-edge areas.

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AWS Machine Learning Blog

MAY 15, 2024

ONNX is an open source machine learning (ML) framework that provides interoperability across a wide range of frameworks, operating systems, and hardware platforms. AWS Graviton3 processors are optimized for ML workloads, including support for bfloat16, Scalable Vector Extension (SVE), and Matrix Multiplication (MMLA) instructions.

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AWS Machine Learning Blog

DECEMBER 16, 2024

70B through SageMaker JumpStart offers two convenient approaches: using the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Prior to joining AWS, Dr. Li held data science roles in the financial and retail industries. Adriana Simmons is a Senior Product Marketing Manager at AWS.

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Towards AI

JANUARY 29, 2025

Generative AI is powered by advanced machine learning techniques, particularly deep learning and neural networks, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Programming Languages: Python (most widely used in AI/ML) R, Java, or C++ (optional but useful) 2.

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Towards AI

AUGUST 8, 2024

Photo by Marius Masalar on Unsplash Deep learning. A subset of machine learning utilizing multilayered neural networks, otherwise known as deep neural networks. If you’re getting started with deep learning, you’ll find yourself overwhelmed with the amount of frameworks. In TensorFlow 2.0,

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Flipboard

APRIL 13, 2023

Prime Air (our drones) and the computer vision technology in Amazon Go (our physical retail experience that lets consumers select items off a shelf and leave the store without having to formally check out) use deep learning. AWS has the broadest and deepest portfolio of AI and ML services at all three layers of the stack.

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AWS Machine Learning Blog

DECEMBER 13, 2023

In this post, we showcase fine-tuning a Llama 2 model using a Parameter-Efficient Fine-Tuning (PEFT) method and deploy the fine-tuned model on AWS Inferentia2. We use the AWS Neuron software development kit (SDK) to access the AWS Inferentia2 device and benefit from its high performance.

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AWS Machine Learning Blog

APRIL 29, 2024

For AWS and Outerbounds customers, the goal is to build a differentiated machine learning and artificial intelligence (ML/AI) system and reliably improve it over time. First, the AWS Trainium accelerator provides a high-performance, cost-effective, and readily available solution for training and fine-tuning large models.

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AWS Machine Learning Blog

JULY 2, 2024

AWS optimized the PyTorch torch.compile feature for AWS Graviton3 processors. the optimizations are available in torch Python wheels and AWS Graviton PyTorch deep learning container (DLC). The goal for the AWS Graviton team was to optimize torch.compile backend for Graviton3 processors.

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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. Access to accelerated instances (GPUs) for hosting the LLMs.

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AWS Machine Learning Blog

JANUARY 17, 2024

Today, we’re excited to announce the availability of Llama 2 inference and fine-tuning support on AWS Trainium and AWS Inferentia instances in Amazon SageMaker JumpStart. In this post, we demonstrate how to deploy and fine-tune Llama 2 on Trainium and AWS Inferentia instances in SageMaker JumpStart.

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AWS Machine Learning Blog

DECEMBER 2, 2024

Today at AWS re:Invent 2024, we are excited to announce the new Container Caching capability in Amazon SageMaker, which significantly reduces the time required to scale generative AI models for inference. On SageMaker, Triton offers a comprehensive serving stack with support for various backends, including TensorRT, PyTorch, Python, and more.

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AWS Machine Learning Blog

APRIL 4, 2025

When we launched LLM-as-a-judge (LLMaJ) and Retrieval Augmented Generation (RAG) evaluation capabilities in public preview at AWS re:Invent 2024 , customers used them to assess their foundation models (FMs) and generative AI applications, but asked for more flexibility beyond Amazon Bedrock models and knowledge bases.

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AWS Machine Learning Blog

MAY 3, 2023

AWS, Arm, Meta and others helped optimize the performance of PyTorch 2.0 As a result, we are delighted to announce that AWS Graviton-based instance inference performance for PyTorch 2.0 times the speed for BERT, making Graviton-based instances the fastest compute optimized instances on AWS for these models. is up to 3.5

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Towards AI

NOVEMBER 5, 2023

Source: [link] This article describes a solution for a generative AI resume screener that got us 3rd place at DataRobot & AWS Hackathon 2023. You can also set the environment variables on the notebook instance for things like AWS access key etc. Source: author’s screenshot on AWS We used Anthropic Claude 2 in our solution.

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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.

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AWS Machine Learning Blog

MAY 1, 2024

Llama2 by Meta is an example of an LLM offered by AWS. To learn more about Llama 2 on AWS, refer to Llama 2 foundation models from Meta are now available in Amazon SageMaker JumpStart. Virginia) and US West (Oregon) AWS Regions, and most recently announced general availability in the US East (Ohio) Region.

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AWS Machine Learning Blog

MAY 31, 2023

In late 2022, AWS announced the general availability of Amazon EC2 Trn1 instances powered by AWS Trainium accelerators, which are purpose built for high-performance deep learning training. Prerequisites To follow along, familiarity with core AWS services such as Amazon EC2 and Amazon ECS is implied.

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AWS Machine Learning Blog

MAY 4, 2023

AWS has been innovating with purpose-built chips to address the growing need for powerful, efficient, and cost-effective compute hardware. You can use ml.trn1 and ml.inf2 compatible AWS Deep Learning Containers (DLCs) for PyTorch, TensorFlow, Hugging Face, and large model inference (LMI) to easily get started.

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AWS Machine Learning Blog

APRIL 10, 2023

We explained in a previous post how you can use Amazon SageMaker deep learning containers (DLCs) to deploy these kinds of large models using a GPU-based instance. In this post, we take the same approach but host the model on AWS Inferentia2. For benchmark performance figures, refer to AWS Neuron Performance.

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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. release, AWS customers can now do same things as they could with PyTorch 1.x 24xlarge with AWS PyTorch 2.0 on AWS PyTorch2.0

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AWS Machine Learning Blog

NOVEMBER 8, 2023

AWS and NVIDIA have come together to make this vision a reality. AWS, NVIDIA, and other partners build applications and solutions to make healthcare more accessible, affordable, and efficient by accelerating cloud connectivity of enterprise imaging. AHI provides API access to ImageSet metadata and ImageFrames.

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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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AWS Machine Learning Blog

NOVEMBER 29, 2023

In today’s rapidly evolving landscape of artificial intelligence, deep learning models have found themselves at the forefront of innovation, with applications spanning computer vision (CV), natural language processing (NLP), and recommendation systems. If not, refer to Using the SageMaker Python SDK before continuing.

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AWS Machine Learning Blog

JUNE 13, 2023

The size of the machine learning (ML) models––large language models ( LLMs ) and foundation models ( FMs )–– is growing fast year-over-year , and these models need faster and more powerful accelerators, especially for generative AI. With AWS Inferentia1, customers saw up to 2.3x With AWS Inferentia1, customers saw up to 2.3x

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AWS Machine Learning Blog

OCTOBER 10, 2023

Machine learning (ML), especially deep learning, requires a large amount of data for improving model performance. Customers often need to train a model with data from different regions, organizations, or AWS accounts. Federated learning (FL) is a distributed ML approach that trains ML models on distributed datasets.

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PyImageSearch

SEPTEMBER 30, 2024

Home Table of Contents Introduction to GitHub Actions for Python Projects Introduction What Is CICD? For Python projects, CI/CD pipelines ensure that your code is consistently integrated and delivered with high quality and reliability. Git is the most commonly used VCS for Python projects, enabling collaboration and version tracking.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

Zeta’s AI innovations over the past few years span 30 pending and issued patents, primarily related to the application of deep learning and generative AI to marketing technology. Zeta’s AI innovation is powered by a proprietary machine learning operations (MLOps) system, developed in-house.

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AWS Machine Learning Blog

FEBRUARY 23, 2023

You can use the SageMaker Python SDK to trigger a job with data parallelism with minimal modifications to the training script. Data parallelism supports popular deep learning frameworks PyTorch, PyTorch Lightening, TensorFlow, and Hugging Face Transformers.

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AWS Machine Learning Blog

JULY 24, 2023

When deploying Deep Learning models at scale, it is crucial to effectively utilize the underlying hardware to maximize performance and cost benefits. In this post we walk you through the process of deploying FastAPI model servers on AWS Inferentia devices (found on Amazon EC2 Inf1 and Amazon EC Inf2 instances).

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AWS Machine Learning Blog

OCTOBER 10, 2024

To achieve this, the Rufus team is using multiple AWS services and AWS AI chips, AWS Trainium and AWS Inferentia. Inferentia and Trainium are purpose-built chips developed by AWS that accelerate deep learning workloads with high performance and lower overall costs.

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AWS Machine Learning Blog

NOVEMBER 12, 2024

You may check out additional reference notebooks on aws-samples for how to use Meta’s Llama models hosted on Amazon Bedrock. You can implement these steps either from the AWS Management Console or using the latest version of the AWS Command Line Interface (AWS CLI). Solutions Architect at AWS. Varun Mehta is a Sr.

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AWS Machine Learning Blog

OCTOBER 26, 2023

Our innovative new A-POPs (or vending machines) deliver enhanced customer experiences at ten times lower cost because of the performance and cost advantages AWS Inferentia delivers. Unlocking high-performance and cost-effective inference using AWS Inferentia As retailers look to scale operations, cost of A-POPs becomes a consideration.

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AWS Machine Learning Blog

JULY 26, 2023

In this post, we show how you can run Stable Diffusion models and achieve high performance at the lowest cost in Amazon Elastic Compute Cloud (Amazon EC2) using Amazon EC2 Inf2 instances powered by AWS Inferentia2. versions on AWS Inferentia2 cost-effectively. You can run both Stable Diffusion 2.1 The Stable Diffusion 2.1

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