Category: Amazon SageMaker

Today AWS announces new features in Amazon SageMaker Canvas that help business analysts generate insights from thousands of documents, images, and lines of text in minutes with machine learning (ML). Starting today, you can access ready-to-use models and create custom text and image classification models alongside previously supported custom models for tabular data, all without […]

A couple days ago, I had the honor of doing a live stream on generative AI, discussing recent innovations and concepts behind the current generation of large language and vision models and how we got there. In today’s roundup of news and announcements, I will share some additional information—including an expanded partnership to make generative […]

Today, we celebrate Martin Luther King Jr. Day in the US to honor the late civil rights leader’s life, legacy, and achievements. In this article, Amazon employees share what MLK Day means to them and how diversity makes us stronger. Coming back to our AWS Week in Review—it’s been a busy week! Last Week’s Launches […]

Amazon SageMaker Canvas provides business analysts with a visual interface to solve business problems using machine learning (ML) without writing a single line of code. Since we introduced SageMaker Canvas in 2021, many users have asked us for an enhanced, seamless collaboration experience that enables data scientists to share trained models with their business analysts […]

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS! The world is asynchronous, is what Werner Vogels, Amazon CTO, reminded us during his keynote last week at AWS re:Invent. At the beginning of the keynote, he showed us how […]

As you move your machine learning (ML) workloads into production, you need to continuously monitor your deployed models and iterate when you observe a deviation in your model performance. When you build a new model, you typically start validating the model offline using historical inference request data. But this data sometimes fails to account for […]

In 2019, we introduced Amazon SageMaker Studio, the first fully integrated development environment (IDE) for data science and machine learning (ML). SageMaker Studio gives you access to fully managed Jupyter Notebooks that integrate with purpose-built tools to perform all ML steps, from preparing data to training and debugging models, tracking experiments, deploying and monitoring models, […]

Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. SageMaker JumpStart gives you access to built-in algorithms with pre-trained models from popular model hubs, pre-trained foundation models to help you perform tasks such as article summarization and image generation, and end-to-end solutions to solve common use cases. […]