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  • Configure SMTP on Amazon Simple Email Service

    This article shows you how to setup your SMTP server on AWS.

  • Use PostScript™ for Visualization of Simulation Results

    While there is an abundance of data visualization packages available for various programming environments, if you find yourself in need of a distinctive visualization while keeping your code stack minimal, PostScript provides an elegant answer.

  • Overcome the blank-page syndrome and avoid plagiarism…with help of ChatGPT

    Much is written about ChatGPT and other generative artificial intelligence (GenAI) models. That made me wonder if these tools can help to write an article.

  • Detecting data drift using Amazon SageMaker

    As companies continue to embrace the cloud and digital transformation, they use historical data in order to identify trends and insights. This data is foundational to power tools, such as data analytics and machine learning (ML), in order to achieve high quality results.

  • Build workflows for Amazon Forecast with AWS Step Functions

    This blog builds a full lifecycle workflow for Amazon Forecast to predict household electricity consumption from historic data. Previously, developers used AWS Lambda function to build workflows for Amazon Forecast. You can now use AWS Step Functions with AWS SDK integrations to reduce cost and complexity.

  • Use ML predictions over Amazon DynamoDB data with Amazon Athena ML

    Today’s modern applications use multiple purpose-built database engines, including relational, key-value, document, and in-memory databases. This purpose-built approach improves the way applications use data by providing better performance and reducing cost. However, the approach raises some challenges for data teams that need to provide a holistic view on top of these database engines, and especially when they need to merge the data with datasets in the organization’s data lake.