SHREC is a physics-based unsupervised learning framework that reconstructs unobserved causal drivers from complex time series data. This new approach addresses the limitations of contemporary techniques, such as noise susceptibility and high computational cost, by using recurrence structures and topological embeddings. The successful application of SHREC on diverse datasets highlights its wide applicability and reliability in fields like biology, physics, and engineering, improving the accuracy of causal driver reconstruction.
A comprehensive walkthrough for building a multicluster GitOps platform using popular open source tools in the Kubernetes space, focusing on choosing a cloud provider, selecting a Git provider, establishing a platform domain and DNS provider, defining Infrastructure as Code, selecting a GitOps engine, and defining management pillars.
A Microsoft engineer demonstrates how WebAssembly modules can run alongside containers in Kubernetes environments, offering benefits like reduced size and faster cold start times for certain workloads.
An introduction to using Terraform for Infrastructure as Code (IaC) practices, providing beginners with a guide to start with Terraform.
Discussion on the challenges and promises of deep learning for outlier detection in various data modalities, including image and tabular data, with a focus on self-supervised learning techniques.
An introduction to Ntfy, a self-hosted push notification server. Learn how to set it up using Docker, configure authentication, and start sending and receiving notifications.
The article discusses the future of observability in 2025, highlighting the significant role of OpenTelemetry and AI in improving observability and reducing costs.
An article on building an AI agent to interact with Apache Airflow using PydanticAI and Gemini 2.0, providing a structured and reliable method for managing DAGs through natural language queries.
- Agent interacts with Apache Airflow via the Airflow REST API.
- Agent can understand natural language queries about workflows, fetch real-time status updates, and return structured data.
- Sample DAGs are implemented for demonstration purposes.
- Agent interacts with Apache Airflow via the Airflow REST API.
- Agent can understand natural language queries about workflows, fetch real-time status updates, and return structured data.
- Sample DAGs are implemented for demonstration purposes.
A reflection on the key achievements, contributions, and community efforts of 2024 for OpenTelemetry, including multilingual documentation and IA improvements.
Breser is a powerful and flexible query language designed for efficient log processing and structured data filtering. It provides an intuitive syntax that combines the familiarity of programming languages with the specific needs of log analysis and data querying.
This chapter covers the fundamental syntax elements and field access mechanisms for accessing and manipulating data fields within structured data.
This chapter covers the fundamental syntax elements and field access mechanisms for accessing and manipulating data fields within structured data.
