Machine Learning

Building Scalable Serverless AI/ML Pipelines As the demand for artificial intelligence (AI) and machine learning (ML) applications continues to grow, the need for scalable and efficient pipelines has never been more pressing. In this article, we will explore the benefits and challenges of building scalable serverless AI/ML pipelines and provide a step-by-step guide on how to implement them. Introduction Serverless architecture is a key enabler for scalable AI/ML pipelines, allowing data engineers to focus on building and deploying applications without managing infrastructure. By leveraging serverless computing services like AWS Lambda, Google Cloud Functions, and Azure Functions, we can create scalable and cost-effective pipelines that can handle large volumes of data. ...

As a SOC analyst, one of the most critical tasks is analyzing network flow data to identify potential security threats. In this post, we’ll explore how to combine cloud-based data storage, SQL querying, and AI-powered analysis to streamline this process. Collecting Flow Data in Amazon Athena Amazon Athena provides a serverless query service that makes it easy to analyze data directly in Amazon S3 using standard SQL. Here’s how we set up our flow data collection: ...