AWS Machine Learning Blog

MAY 15, 2024

AWS Graviton3 processors are optimized for ML workloads, including support for bfloat16, Scalable Vector Extension (SVE), and Matrix Multiplication (MMLA) instructions. In this post, we show how to run ONNX Runtime inference on AWS Graviton3-based EC2 instances and how to configure them to use optimized GEMM kernels.

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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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IBM Data Science in Practice

APRIL 4, 2023

Introduction This article shows how to monitor a model deployed on AWS Sagemaker for quality, bias and explainability, using IBM Watson OpenScale on the IBM Cloud Pak for Data platform. This article shows how to use the endpoint generated from that tutorial to demonstrate how to monitor the AWS deployment with Watson OpenScale.

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AWS Machine Learning Blog

JANUARY 29, 2025

In this post, we explore how to deploy distilled versions of DeepSeek-R1 with Amazon Bedrock Custom Model Import, making them accessible to organizations looking to use state-of-the-art AI capabilities within the secure and scalable AWS infrastructure at an effective cost. You can monitor costs with AWS Cost Explorer.

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IBM Data Science in Practice

MARCH 8, 2023

You can then export the model and deploy it on Amazon Sagemaker on Amazon Web Server (AWS). If you are set up with the required systems, you can download the sample project and complete the steps for hands-on learning. This sample uses the following platforms: Watson Studio and Watson Machine Learning with a Python 3.9

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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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Flipboard

JUNE 26, 2023

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. Run the AWS Glue ML transform job.

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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

JUNE 23, 2023

Additionally, you can use AWS Lambda directly to expose your models and deploy your ML applications using your preferred open-source framework, which can prove to be more flexible and cost-effective. FastAPI is a modern, high-performance web framework for building APIs with Python. This will be used to deploy our solution.

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AWS Machine Learning Blog

OCTOBER 10, 2023

Customers often need to train a model with data from different regions, organizations, or AWS accounts. Existing partner open-source FL solutions on AWS include FedML and NVIDIA FLARE. These open-source packages are deployed in the cloud by running in virtual machines, without using the cloud-native services available on AWS.

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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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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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DrivenData Labs

NOVEMBER 14, 2023

VIIRS' Day/Night Band (DNB) sensor captures nightlight imagery that is useful for mapping populations. ECOSTRESS has been used in the midwestern United States to detects droughts early enough to recover and protect crops.

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AWS Machine Learning Blog

MARCH 8, 2023

This post shows a way to do this using Snowflake as the data source and by downloading the data directly from Snowflake into a SageMaker Training job instance. We create a custom training container that downloads data directly from the Snowflake table into the training instance rather than first downloading the data into an S3 bucket.

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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. petaflops for BF16/FP16.

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AWS Machine Learning Blog

MARCH 18, 2025

With this launch, you can now deploy NVIDIAs optimized reranking and embedding models to build, experiment, and responsibly scale your generative AI ideas on AWS. As part of NVIDIA AI Enterprise available in AWS Marketplace , NIM is a set of user-friendly microservices designed to streamline and accelerate the deployment of generative AI.

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AWS Machine Learning Blog

JUNE 4, 2024

Terraform is an IaC tool that allows you to manage AWS resources, software as a service (SaaS) resources, datasets, and more, using declarative configuration. Configure your local Python virtual environment. Download the DialogSum public dataset and convert it to JSONL. python v3.8 Terraform v1.0.0 or newer installed.

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AWS Machine Learning Blog

MARCH 15, 2024

In addition to Amazon Bedrock, you can use other AWS services like Amazon SageMaker JumpStart and Amazon Lex to create fully automated and easily adaptable generative AI order processing agents. In this post, we show you how to build a speech-capable order processing agent using Amazon Lex, Amazon Bedrock, and AWS Lambda.

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AWS Machine Learning Blog

AUGUST 30, 2023

This post demonstrates a strategy for fine-tuning publicly available LLMs for the task of radiology report summarization using AWS services. In the following sections, we demonstrate fine-tuning an LLM available on SageMaker JumpStart for summarization of a domain-specific task via the SageMaker Python SDK. We use an ml.t3.medium

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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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Flipboard

MARCH 7, 2023

This post presents and compares options and recommended practices on how to manage Python packages and virtual environments in Amazon SageMaker Studio notebooks. You can manage app images via the SageMaker console, the AWS SDK for Python (Boto3), and the AWS Command Line Interface (AWS CLI). Define a Dockerfile.

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AWS Machine Learning Blog

MARCH 3, 2025

These recipes include a training stack validated by Amazon Web Services (AWS) , which removes the tedious work of experimenting with different model configurations, minimizing the time it takes for iterative evaluation and testing. Alternatively, you can also use AWS Systems Manager and run a command like the following to start the session.

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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

APRIL 7, 2023

To reduce the barrier to entry of ML at the edge, we wanted to demonstrate an example of deploying a pre-trained model from Amazon SageMaker to AWS Wavelength , all in less than 100 lines of code. In this post, we demonstrate how to deploy a SageMaker model to AWS Wavelength to reduce model inference latency for 5G network-based applications.

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Flipboard

DECEMBER 11, 2024

Discover your data and put it to work using familiar AWS tools to complete end-to-end development workflows, including data analysis, data processing, model training, generative AI app building, and more, in a single governed environment. Youre redirected to the AWS CloudFormation console to deploy a stack to configure VPC resources.

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AWS Machine Learning Blog

AUGUST 26, 2024

AWS makes it possible for organizations of all sizes and developers of all skill levels to build and scale generative AI applications with security, privacy, and responsible AI. In this post, we dive into the architecture and implementation details of GenASL, which uses AWS generative AI capabilities to create human-like ASL avatar videos.

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AWS Machine Learning Blog

SEPTEMBER 20, 2023

For example, you might have acquired a company that was already running on a different cloud provider, or you may have a workload that generates value from unique capabilities provided by AWS. We show how you can build and train an ML model in AWS and deploy the model in another platform.

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AWS Machine Learning Blog

SEPTEMBER 23, 2024

Prerequisties The proposed solution can be implemented in a personal AWS environment using the code that we provide. Before running the labs in this post, ensure you have the following: An AWS account. The appropriate AWS Identity and Access Management (IAM) permissions to access services used in the lab.

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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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Flipboard

NOVEMBER 20, 2023

We use two AWS Media & Entertainment Blog posts as the sample external data, which we convert into embeddings with the BAAI/bge-small-en-v1.5 Prerequisites To follow the steps in this post, you need to have an AWS account and an AWS Identity and Access Management (IAM) role with permissions to create and access the solution resources.

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AWS Machine Learning Blog

MAY 9, 2023

In this post, we help you understand the Python backend that is supported by Triton on SageMaker so that you can make an informed decision for your workloads and achieve great results. Triton supports instance types that support GPUs, CPUs, and AWS Inferentia chips, which allow you to maximize the performance for your workloads.

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AWS Machine Learning Blog

JULY 2, 2024

AWS Amplify to create and deploy the web application. Amazon API Gateway and AWS Lambda to create an API with an authentication layer and integrate with Amazon Bedrock. The AWS Well-Architected Framework documentation. The Implementing Microservices on AWS whitepaper. Refer to the Amazon Bedrock FAQs for further details.

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AWS Machine Learning Blog

MAY 31, 2023

You can submit a document from the AWS Management Console , AWS Command Line Interface (AWS CLI), or AWS SDK and receive the translated document in real time while maintaining the format of the original document. The translated file is automatically saved to your browser’s downloaded folder, usually to Downloads.

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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. PyAnnote is an open source toolkit written in Python for speaker diarization.

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The MLOps Blog

MAY 10, 2023

How to save a trained model in Python? Saving trained model with pickle The pickle module can be used to serialize and deserialize the Python objects. For saving the ML models used as a pickle file, you need to use the Pickle module that already comes with the default Python installation. Now let’s see how we can save our model.

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AWS Machine Learning Blog

APRIL 27, 2023

AWS Trainium and AWS Inferentia2 , which are purpose built for DL training and inference, extend their functionality and performance by supporting custom operators (or CustomOps, for short). AWS Neuron , the SDK that supports these accelerators, uses the standard PyTorch interface for CustomOps.

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AWS Machine Learning Blog

FEBRUARY 20, 2024

With an aim to accelerate the localization of content workflows through machine learning, ZOO Digital engaged AWS Prototyping, an investment program by AWS to co-build workloads with customers. This S3 bucket was configured to emit an event when new files are detected within it, triggering an AWS Lambda function.

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