Latest Posts
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…
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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…
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Announcing Additional Data Connectors for Amazon AppFlow
Gathering insights from data is a more effective process if that data isn’t fragmented across multiple systems and data stores, whether on premises or in the cloud. Amazon AppFlow provides bidirectional data integration between on-premises systems and applications, SaaS applications,…
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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…
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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 software continuously meets your industry standards by compiling…
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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…
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New — Amazon SageMaker Data Wrangler Supports SaaS Applications as Data Sources
Data fuels machine learning. In machine learning, data preparation is the process of transforming raw data into a format that is suitable for further processing and analysis. The common process for data preparation starts with collecting data, then cleaning it,…
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New AWS SimSpace Weaver–Run Large-Scale Spatial Simulations in the Cloud
Today, we’re announcing AWS SimSpace Weaver, a new compute service to run real-time spatial simulations in the cloud and at scale. With SimSpace Weaver, simulation developers are no longer limited by the compute and memory of their hardware. Organizations run…
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New – Amazon EC2 Hpc6id Instances Optimized for High Performance Computing
We have given you the flexibility and ability to run the largest and most complex high performance computing (HPC) workloads with Amazon Elastic Compute Cloud (Amazon EC2) instances that feature enhanced networking like C5n, C6gn, R5n, M5n, and our recently launched HPC…
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New for Amazon Redshift – General Availability of Streaming Ingestion for Kinesis Data Streams and Managed Streaming for Apache Kafka
Ten years ago, just a few months after I joined AWS, Amazon Redshift was launched. Over the years, many features have been added to improve performance and make it easier to use. Amazon Redshift now allows you to analyze structured…
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