AWS Machine Learning Blog

Category: Foundational (100)

Introducing Fortuna: A library for uncertainty quantification

Proper estimation of predictive uncertainty is fundamental in applications that involve critical decisions. Uncertainty can be used to assess the reliability of model predictions, trigger human intervention, or decide whether a model can be safely deployed in the wild. We introduce Fortuna, an open-source library for uncertainty quantification. Fortuna provides calibration methods, such as conformal […]

LightOn Lyra-fr model is now available on Amazon SageMaker

We are thrilled to announce the availability of the LightOn Lyra-fr foundation model for customers using Amazon SageMaker. LightOn is a leader in building foundation models specializing in European languages. Lyra-fr is a state-of-the-art French language model that can be used to build conversational AI, copywriting tools, text classifiers, semantic search, and more. You can […]

Start your successful journey with time series forecasting with Amazon Forecast

Organizations of all sizes are striving to grow their business, improve efficiency, and serve their customers better than ever before. Even though the future is uncertain, a data-driven, science-based approach can help anticipate what lies ahead to successfully navigate through a sea of choices. Every industry uses time series forecasting to address a variety of […]

Build an agronomic data platform with Amazon SageMaker geospatial capabilities

The world is at increasing risk of global food shortage as a consequence of geopolitical conflict, supply chain disruptions, and climate change. Simultaneously, there’s an increase in overall demand from population growth and shifting diets that focus on nutrient- and protein-rich food. To meet the excess demand, farmers need to maximize crop yield and effectively […]

Separate lines of business or teams with multiple Amazon SageMaker domains

Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning (ML) that enables data scientists and developers to perform every step of the ML workflow, from preparing data to building, training, tuning, and deploying models. To access SageMaker Studio, Amazon SageMaker Canvas, or other Amazon ML environments like RStudio on Amazon SageMaker, […]

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 […]

“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 […]

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

January 2023: This post was reviewed and updated with support for upscaling using Stable Diffusion models. 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 […]

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 […]

Transfer learning for TensorFlow object detection models in Amazon SageMaker

Amazon SageMaker provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning. They can process various types of input data, including tabular, […]