Build high performing image classification models using Amazon SageMaker JumpStart

Favorite 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

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Shared by AWS Machine Learning November 17, 2022

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

Favorite 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

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Shared by AWS Machine Learning November 17, 2022

AlexaTM 20B is now available in Amazon SageMaker JumpStart

Favorite 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

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Shared by AWS Machine Learning November 17, 2022

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

Favorite 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

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Shared by AWS Machine Learning November 16, 2022

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

Favorite 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.

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Shared by AWS Machine Learning November 16, 2022

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

Favorite 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

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Shared by AWS Machine Learning November 15, 2022

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

Favorite 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

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Shared by AWS Machine Learning November 15, 2022