Artificial Intelligence

Category: Learning Levels

Identifying and avoiding common data issues while building no code ML models with Amazon SageMaker Canvas

Business analysts work with data and like to analyze, explore, and understand data to achieve effective business outcomes. To address business problems, they often rely on machine learning (ML) practitioners such as data scientists to assist with techniques such as utilizing ML to build models using existing data and generate predictions. However, it isn’t always […]

Brain tumor segmentation at scale using AWS Inferentia

Medical imaging is an important tool for the diagnosis and localization of disease. Over the past decade, collections of medical images have grown rapidly, and open repositories such as The Cancer Imaging Archive and Imaging Data Commons have democratized access to this vast imaging data. Computational tools such as machine learning (ML) and artificial intelligence […]

“ID + Selfie” – Improving digital identity verification using AWS

The COVID-19 global pandemic has accelerated the need to verify and onboard users online across several industries, such as financial services, insurance, and healthcare. When it comes to user experience it is crucial to provide a frictionless transaction while maintaining a high standard for identity verification.  The question is, how do you verify real people […]

Model hosting patterns in Amazon SageMaker, Part 2: Getting started with deploying real time models on SageMaker

Amazon SageMaker is a fully-managed service that provides every developer and data scientist with the ability to quickly build, train, and deploy machine learning (ML) models at scale. ML is realized in inference. SageMaker offers four Inference options: Real-Time Inference Serverless Inference Asynchronous Inference Batch Transform These four options can be broadly classified into Online […]

Predict lung cancer survival status using multimodal data on Amazon SageMaker JumpStart

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, and is composed of tumors with significant molecular heterogeneity resulting from differences in intrinsic oncogenic signaling pathways [1]. Enabling precision medicine, anticipating patient preferences, detecting disease, and improving care quality for NSCLC patients are important topics among healthcare and life sciences (HCLS) […]

Generate images from text with the stable diffusion model on Amazon SageMaker JumpStart

March 2023: This post was reviewed and updated with support for Stable Diffusion inpainting model. Today, we announce that Stable Diffusion 1 and Stable Diffusion 2 are available in Amazon SageMaker JumpStart. JumpStart is the machine learning (ML) hub of SageMaker that provides hundreds of built-in algorithms, pre-trained models, and end-to-end solution templates to help you quickly get started with […]

Run text generation with Bloom and GPT models on Amazon SageMaker JumpStart

Today, we announce that large language models Bloom and GPT-2 are available in SageMaker JumpStart. Amazon SageMaker JumpStart is the machine learning hub of SageMaker that provides hundreds of built-in algorithms, pre-trained models, and end-to-end solution templates to help customers quickly get started with machine learning (ML). You can use these models for a wide […]

Deploy BLOOM-176B and OPT-30B on Amazon SageMaker with large model inference Deep Learning Containers and DeepSpeed

April 2023: This post was reviewed and updated for accuracy. The last few years have seen rapid development in the field of deep learning. Although hardware has improved, such as with the latest generation of accelerators from NVIDIA and Amazon, advanced machine learning (ML) practitioners still regularly encounter issues deploying their large deep learning models […]

Transfer learning for TensorFlow object detection models in Amazon SageMaker

July 2023: You can also use the newly launched JumpStart APIs, an extension of the SageMaker Python SDK. These APIs allow you to programmatically deploy and fine-tune a vast selection of JumpStart-supported pre-trained models on your own datasets. Please refer to Amazon SageMaker JumpStart models and algorithms now available via API for more details on how […]

Transfer learning for TensorFlow text classification models in Amazon SageMaker

July 2023: You can also use the newly launched JumpStart APIs, an extension of the SageMaker Python SDK. These APIs allow you to programmatically deploy and fine-tune a vast selection of JumpStart-supported pre-trained models on your own datasets. Please refer to Amazon SageMaker JumpStart models and algorithms now available via API for more details on how […]