Latest Posts
Transaction Support in Cloudera Operational Database (COD)
What is CDP Operational Database (COD) CDP Operational Database enables developers to quickly build future-proof applications that are architected to handle data evolution. It helps developers automate and simplify database management with capabilities like auto-scale, and is fully integrated with…
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How to Deploy Transaction Support on Cloudera Operational Database (COD)
What is Cloudera Operational Database (COD) Cloudera Operational Database enables developers to quickly build future-proof applications that are architected to handle data evolution. It helps developers automate and simplify database management with capabilities like auto-scale, and is fully integrated with…
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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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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…
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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 — 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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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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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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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 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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