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...
Introducing Amazon GameLift Anywhere – Run Your Game Servers on Your Own Infrastructure
In 2016, we launched Amazon GameLift, a dedicated hosting solution that securely deploys and automatically scales fleets of session-based multiplayer game servers to meet worldwide player demand. With Amazon GameLift,...
New — Create Point-to-Point Integrations Between Event Producers and Consumers with Amazon EventBridge Pipes
It is increasingly common to use multiple cloud services as building blocks to assemble a modern event-driven application. Using purpose-built services to accomplish a particular task ensures developers get the best...
Step Functions Distributed Map – A Serverless Solution for Large-Scale Parallel Data Processing
I am excited to announce the availability of a distributed map for AWS Step Functions. This flow extends support for orchestrating large-scale parallel workloads such as the on-demand processing of semi-structured data....
AWS Marketplace Vendor Insights – Simplify Third-Party Software Risk Assessments
AWS Marketplace Vendor Insights is a new capability of AWS Marketplace. It simplifies third-party software risk assessments when procuring solutions from the AWS Marketplace. It helps you to ensure that the third-party...
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...
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...
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,...
AWS Machine Learning University New Educator Enablement Program to Build Diverse Talent for ML/AI Jobs
AWS Machine Learning University is now providing a free educator enablement program. This program provides faculty at community colleges, minority-serving institutions (MSIs), and historically Black colleges and...
New for Amazon Redshift – Simplify Data Ingestion and Make Your Data Warehouse More Secure and Reliable
When we talk with customers, we hear that they want to be able to harness insights from data in order to make timely, impactful, and actionable business decisions. A common pattern with data-driven organizations is that...
New — Introducing Support for Real-Time and Batch Inference in Amazon SageMaker Data Wrangler
To build machine learning models, machine learning engineers need to develop a data transformation pipeline to prepare the data. The process of designing this pipeline is time-consuming and requires a cross-team...
