Perform generative AI-powered data prep and no-code ML over any size of data using Amazon SageMaker Canvas

By Admin 16/08/2024

AWS Machine Learning Blog Amazon SageMaker Canvas now empowers enterprises to harness the full potential of their data by enabling support of petabyte-scale datasets. Starting today, you can interactively prepare large datasets, create end-to-end data flows, and invoke automated machine learning (AutoML) experiments on petabytes of data—a substantial leap from the previous 5 GB limit. […]Continue reading

Amazon SageMaker inference launches faster auto scaling for generative AI models

By Admin 26/07/2024

AWS Machine Learning Blog Today, we are excited to announce a new capability in Amazon SageMaker inference that can help you reduce the time it takes for your generative artificial intelligence (AI) models to scale automatically. You can now use sub-minute metrics and significantly reduce overall scaling latency for generative AI models. With this enhancement, […]Continue reading

Helping nonexperts build advanced generative AI models

By Admin 21/06/2024

MIT News – Artificial intelligence The impact of artificial intelligence will never be equitable if there’s only one company that builds and controls the models (not to mention the data that go into them). Unfortunately, today’s AI models are made up of billions of parameters that must be trained and tuned to maximize performance for […]Continue reading

Manage Amazon SageMaker JumpStart foundation model access with private hubs

By Admin 21/06/2024

AWS Machine Learning Blog Amazon SageMaker JumpStart is a machine learning (ML) hub offering pre-trained models and pre-built solutions. It provides access to hundreds of foundation models (FMs). A private hub is a feature in SageMaker JumpStart that allows an organization to share their models and notebooks so as to centralize model artifacts, facilitate discoverability, […]Continue reading

Build a Hugging Face text classification model in Amazon SageMaker JumpStart

By Admin 09/05/2024

AWS Machine Learning Blog Amazon SageMaker JumpStart 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 […]Continue reading

Amazon SageMaker now integrates with Amazon DataZone to streamline machine learning governance

By Admin 08/05/2024

AWS Machine Learning Blog Amazon SageMaker is a fully managed machine learning (ML) service that provides a range of tools and features for building, training, and deploying ML models. Amazon DataZone is a data management service that makes it faster and easier for customers to catalog, discover, share, and govern data stored across AWS, on-premises, […]Continue reading

Use Kubernetes Operators for new inference capabilities in Amazon SageMaker that reduce LLM deployment costs by 50% on average

By Admin 19/04/2024

AWS Machine Learning Blog We are excited to announce a new version of the Amazon SageMaker Operators for Kubernetes using the AWS Controllers for Kubernetes (ACK). ACK is a framework for building Kubernetes custom controllers, where each controller communicates with an AWS service API. These controllers allow Kubernetes users to provision AWS resources like buckets, […]Continue reading

Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks

By Admin 17/04/2024

AWS Machine Learning Blog Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. In the process of working on their ML tasks, data scientists typically start their workflow by discovering relevant data sources and connecting to them. They then use SQL to explore, […]Continue reading

Boost inference performance for Mixtral and Llama 2 models with new Amazon SageMaker containers

By Admin 08/04/2024

AWS Machine Learning Blog In January 2024, Amazon SageMaker launched a new version (0.26.0) of Large Model Inference (LMI) Deep Learning Containers (DLCs). This version offers support for new models (including Mixture of Experts), performance and usability improvements across inference backends, as well as new generation details for increased control and prediction explainability (such as […]Continue reading

Enable single sign-on access of Amazon SageMaker Canvas using AWS IAM Identity Center: Part 2

By Admin 02/04/2024

AWS Machine Learning Blog Amazon SageMaker Canvas allows you to use machine learning (ML) to generate predictions without having to write any code. It does so by covering the end-to-end ML workflow: whether you’re looking for powerful data preparation and AutoML, managed endpoint deployment, simplified MLOps capabilities, or the ability to configure foundation models for […]Continue reading