AWS News Blog
Category: Announcements
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. Step Function’s map state executes the same processing steps for multiple entries in a dataset. The existing map state is limited to 40 […]
AWS Marketplace Vendor Insights – Simplify Third-Party Software Risk Assessments
Update 8 February 2023: I edited this blog post to remove the “preview” messaging for AWS Artifact third-party reports. —- 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 software continuously meets […]
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. […]
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 universities (HBCUs) with the skills and resources to teach data analytics, artificial intelligence (AI), and machine learning (ML) concepts to build a diverse pipeline for in-demand jobs of […]
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 they have many different data sources they need to ingest into their analytics systems. This requires them to build […]
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 collaboration between machine learning engineers, data engineers, and data scientists to implement the data preparation pipeline into a production environment. The main objective of […]