Category: Learning Levels

Administrators of machine learning (ML) workloads are focused on ensuring that users are operating in the most secure manner, striving towards a principal of least privilege design. They have a wide variety of personas to account for, each with their own unique sets of needs, and building the right sets of permissions policies to meet […]

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 […]

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, […]

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. In addition to the interactive ML experience, data workers also seek solutions to run notebooks as ephemeral jobs without the need to refactor code as Python modules or learn DevOps tools and best practices […]

We’re thrilled to announce that Stability AI has selected AWS as its preferred cloud provider to power its state-of-the-art AI models for image, language, audio, video, and 3D content generation. Stability AI is a community-driven, open-source artificial intelligence (AI) company developing breakthrough technologies. With Amazon SageMaker, Stability AI will build AI models on compute clusters […]

Amazon SageMaker Autopilot, a low-code machine learning (ML) service that automatically builds, trains, and tunes the best ML models based on tabular data, is now integrated with Amazon SageMaker Pipelines, the first purpose-built continuous integration and continuous delivery (CI/CD) service for ML. This enables the automation of an end-to-end flow of building ML models using […]

Today we are excited to announce that AI21 Jurassic-1 (J1) foundation models are available for customers using Amazon SageMaker. Jurassic-1 models are highly versatile, capable of both human-like text generation, as well as solving complex tasks such as question answering, text classification, and many others. You can easily try out this model and use it […]

Machine learning (ML) models are taking the world by storm. Their performance relies on using the right training data and choosing the right model and algorithm. But it doesn’t end here. Typically, algorithms defer some design decisions to the ML practitioner to adopt for their specific data and task. These deferred design decisions manifest themselves […]

June 2023: This post was reviewed and updated to reflect the launch of EMR release 6.10 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. Studio […]

Amazon Forecast is a fully managed service that uses statistical and machine learning (ML) algorithms to deliver highly accurate time-series forecasts. Recently, based on Amazon Forecast, we helped one of our retail customers achieve accurate demand forecasting, within 8 weeks. The solution improved the manual forecast by an average of 10% in regards to the […]