AWS News Blog
Category: Artificial Intelligence
Week in Review – February 13, 2023
AWS announced 32 capabilities since we published the last Week in Review blog post a week ago. I also read a couple of other news and blog posts. Here is my summary. The VPC section of the AWS Management Console now allows you to visualize your VPC resources, such as the relationships between a VPC […]
AWS Week in Review – January 16, 2023
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 […]
New – Bring ML Models Built Anywhere into Amazon SageMaker Canvas and Generate Predictions
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 […]
AWS Week in Review – December 12, 2022
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 […]
New – Process PDFs, Word Documents, and Images with Amazon Comprehend for IDP
Today we are announcing a new Amazon Comprehend feature for intelligent document processing (IDP). This feature allows you to classify and extract entities from PDF documents, Microsoft Word files, and images directly from Amazon Comprehend without you needing to extract the text first. Many customers need to process documents that have a semi-structured format, like […]
New for Amazon SageMaker – Perform Shadow Tests to Compare Inference Performance Between ML Model Variants
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 […]
Next Generation SageMaker Notebooks – Now with Built-in Data Preparation, Real-Time Collaboration, and Notebook Automation
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, […]
New – Share ML Models and Notebooks More Easily Within Your Organization with Amazon SageMaker JumpStart
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. […]