Amazon Rekognition Labels adds 600 new labels, including landmarks, and now detects dominant colors

Amazon Rekognition offers pre-trained and customizable computer vision capabilities to extract information and insights from images and videos. One such capability is Amazon Rekognition Labels, which detects objects, scenes, actions, and concepts in images. Customers such as Synchronoss, Shutterstock, and Nomad Media use Amazon Rekognition Labels to automatically add metadata to their content library and … Read more

Generate cold start forecasts for products with no historical data using Amazon Forecast, now up to 45% more accurate

Now with Amazon Forecast, you can generate up to 45% more accurate forecasts for products with no historical data. Forecast is a managed service that uses machine learning (ML) to generate accurate demand forecasts, without requiring any ML experience. Accurate forecasting is the foundation for inventory optimization, logistics planning, and workforce management and it enables … Read more

Identify key insights from text documents through fine-tuning and HPO with Amazon SageMaker JumpStart

Organizations across industries such as retail, banking, finance, healthcare, manufacturing, and lending often have to deal with vast amounts of unstructured text documents coming from various sources, such as news, blogs, product reviews, customer support channels, and social media. These documents contain critical information that’s key to making important business decisions. As an organization grows, … Read more

Your guide to AI/ML at AWS re:Invent 2022

AWS re:Invent season is upon us again! Just a few days to go until re:Invent takes place for the 11th year in Las Vegas, Nevada. The Artificial Intelligence and Machine Learning team at AWS has been working hard to offer amazing content, an outstanding AWS DeepRacer experience, and much more. In this post, we give … Read more

AlexaTM 20B is now available in Amazon SageMaker JumpStart

Today, we announce the public availability of Amazon’s state-of-the-art Alexa Teacher Model with 20 billion parameters  (AlexaTM 20B) through Amazon SageMaker JumpStart, SageMaker’s machine learning hub. AlexaTM 20B is a multilingual large-scale sequence-to-sequence (seq2seq) language model developed by Amazon. You can use AlexaTM 20B for a wide range of industry use-cases, from summarizing financial reports … Read more

How Yara is using MLOps features of Amazon SageMaker to scale energy optimization across their ammonia plants

Yara is the world’s leading crop nutrition company and a provider of environmental and agricultural solutions. Yara’s ambition is focused on growing a nature-positive food future that creates value for customers, shareholders, and society at large, and delivers a more sustainable food value chain. Supporting our vision of a world without hunger and a planet … Read more

Build high performing image classification models using Amazon SageMaker JumpStart

Image classification is a computer vision-based machine learning (ML) technique that allows you to classify images. Some well-known examples of image classification include classifying handwritten digits, medical image classification, and facial recognition. Image classification is a useful technique with several business applications, but building a good image classification model isn’t trivial. Several considerations can play … Read more

Large-scale feature engineering with sensitive data protection using AWS Glue interactive sessions and Amazon SageMaker Studio

Organizations are using machine learning (ML) and AI services to enhance customer experience, reduce operational cost, and unlock new possibilities to improve business outcomes. Data underpins ML and AI use cases and is a strategic asset to an organization. As data is growing at an exponential rate, organizations are looking to set up an integrated, … Read more

Build a cross-account MLOps workflow using the Amazon SageMaker model registry

A well-designed CI/CD pipeline is essential to scale any software development workflow effectively. When designing production CI/CD pipelines, AWS recommends leveraging multiple accounts to isolate resources, contain security threats and simplify billing-and data science pipelines are no different. At AWS, we’re continuing to innovate to simplify the MLOps workflow. In this post, we discuss some … Read more

Enabling hybrid ML workflows on Amazon EKS and Amazon SageMaker with one-click Kubeflow on AWS deployment

Today, many AWS customers are building enterprise-ready machine learning (ML) platforms on Amazon Elastic Kubernetes Service (Amazon EKS) using Kubeflow on AWS (an AWS-specific distribution of Kubeflow) across many use cases, including computer vision, natural language understanding, speech translation, and financial modeling. With the latest release of open-source Kubeflow v1.6.1, the Kubeflow community continues to … Read more

Get more control of your Amazon SageMaker Data Wrangler workloads with parameterized datasets and scheduled jobs

Data is transforming every field and every business. However, with data growing faster than most companies can keep track of, collecting data and getting value out of that data is a challenging thing to do. A modern data strategy can help you create better business outcomes with data. AWS provides the most complete set of … Read more

Detect multicollinearity, target leakage, and feature correlation with Amazon SageMaker Data Wrangler

In machine learning (ML), data quality has direct impact on model quality. This is why data scientists and data engineers spend significant amount of time perfecting training datasets. Nevertheless, no dataset is perfect—there are trade-offs to the preprocessing techniques such as oversampling, normalization, and imputation. Also, mistakes and errors could creep in at various stages … Read more

New Amazon HealthLake capabilities enable next-generation imaging solutions and precision health analytics

At AWS, we have been investing in healthcare since Day 1 with customers including Moderna, Rush University Medical Center, and the NHS who have built breakthrough innovations in the cloud. From developing public health analytics hubs, to improving health equity and patient outcomes, to developing a COVID-19 vaccine in just 65 days, our customers are utilizing … Read more

Refit trained parameters on large datasets using Amazon SageMaker Data Wrangler

Amazon SageMaker Data Wrangler helps you understand, aggregate, transform, and prepare data for machine learning (ML) from a single visual interface. It contains over 300 built-in data transformations so you can quickly normalize, transform, and combine features without having to write any code. Data science practitioners generate, observe, and process data to solve business problems … Read more

Run machine learning inference workloads on AWS Graviton-based instances with Amazon SageMaker

Today, we are launching Amazon SageMaker inference on AWS Graviton to enable you to take advantage of the price, performance, and efficiency benefits that come from Graviton chips. Graviton-based instances are available for model inference in SageMaker. This post helps you migrate and deploy a machine learning (ML) inference workload from x86 to Graviton-based instances … Read more

Amazon SageMaker Studio Lab continues to democratize ML with more scale and functionality

To make machine learning (ML) more accessible, Amazon launched Amazon SageMaker Studio Lab at AWS re:Invent 2021. Today, tens of thousands of customers use it every day to learn and experiment with ML for free. We made it simple to get started with just an email address, without the need for installs, setups, credit cards, … Read more

How Prodege saved $1.5 million in annual human review costs using low-code computer vision AI

This post was co-authored by Arun Gupta, the Director of Business Intelligence at Prodege, LLC. Prodege is a data-driven marketing and consumer insights platform comprised of consumer brands—Swagbucks, MyPoints, Tada, ySense, InboxDollars, InboxPounds, DailyRewards, PollFish, and Upromise—along with a complementary suite of business solutions for marketers and researchers. Prodege has 120 million users and has … Read more

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 … Read more

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 … Read more

Serve multiple models with Amazon SageMaker and Triton Inference Server

Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. It helps data scientists and developers prepare, build, train, and deploy high-quality ML models quickly by bringing together a broad set of capabilities purpose-built for ML. In 2021, AWS announced the integration of NVIDIA Triton Inference Server in SageMaker. You … Read more

Model Hosting Patterns in SageMaker: Best practices in testing and updating models on SageMaker

Amazon SageMaker is a fully managed service that provides developers and data scientists the ability to quickly build, train, and deploy machine learning (ML) models. With SageMaker, you can deploy your ML models on hosted endpoints and get inference results in real time. You can easily view the performance metrics for your endpoints in Amazon … Read more

“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 … Read more

Getting started with deploying real-time models on Amazon 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 … Read more

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) … Read more

Cost-effective data preparation for machine learning using SageMaker Data Wrangler

Amazon SageMaker Data Wrangler is a capability of Amazon SageMaker that makes it faster for data scientists and engineers to prepare high-quality features for machine learning (ML) applications via a visual interface. Data Wrangler reduces the time it takes to aggregate and prepare data for ML from weeks to minutes. With Data Wrangler, you can … Read more

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

In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions that … Read more

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

In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions that … Read more

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

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 for applications such as natural language processing (NLP). In an … Read more