A developer’s work doesn’t stop once the code is written. The real action begins when it’s deployment day. The process—managing multiple environments, testing new features, or ensuring seamless uptime during releases—must be fast, secure, and efficient. Can you imagine a world where deployment is smooth, automated, and risk-free—where strategies scale, adapt, and are successful every … Read more
As AI and machine learning applications continue to proliferate, the data pipelines that power them have become more mission-critical than ever. As retrieval-augmented generation (RAG) applications and real-time AI systems are becoming the norm, any glitch in a data pipeline can lead to stale insights, suboptimal model performance, and inflated infrastructure costs. Working in this … Read more
“If you automate the boring stuff, you can focus on building amazing things.” CI/CD Android Pipeline with Jenkins Let’s learn how to streamline your Android app development with Jenkins. Automate build, versioning, and deployment in a robust CI/CD pipeline. In the fast-paced world of Android development, speed and quality are critical.That’s why setting up a … Read more
Photo by Lucas van Oort on Unsplash We were preparing a long-awaited feature for release — something users had been asking for, and something the team was excited to finally ship. It was a real, commercial Android app — monetized, live on Google Play, with an active user base and very real consequences for mistakes. … Read more
As development cycles speed up, integrating security into the CI/CD pipeline is essential to embed security checks early and throughout the process to prevent vulnerabilities from creeping into production. This article delves deeper into the technical details, code examples, and configurations to build a robust, secure CI/CD pipeline. A CI/CD pipeline is an automated workflow … Read more
With rapid progress in the fields of machine learning (ML) and artificial intelligence (AI), it is important to deploy the AI/ML model efficiently in production environments. This blog post discusses an end-to-end ML pipeline on AWS SageMaker that leverages serverless computing, event-trigger-based data processing, and external API integrations. The architecture downstream ensures scalability, cost efficiency, … Read more
Observability Integration Observability is the cornerstone of reliability and trust in any production-grade retrieval-augmented generation (RAG) pipeline. As these systems become more complex — handling sensitive data, supporting real-time queries, and interfacing with multiple services — being able to trace and measure each step of the data flow and inference process becomes critical. From retrieving … Read more
TL; DR: The Alignment-to-Value Pipeline Effective product development requires both strategic alignment and healthy Product Backlog management. Misalignment leads to backlog bloat, trust erosion, and building the wrong products. By implementing proper alignment tools, separating discovery from delivery, and maintaining appropriate backlog size (3-6 sprints), teams can build products that truly matter. Success depends on … Read more
The Ukrainian drone attack on a pumping station of a pipeline in southern Russia, which carries oil from Kazakhstan’s major oilfields, is a blow at U.S. companies, the oil market, and U.S. President Donald Trump, said Dmitry Medvedev, deputy chairman of the powerful Security Council of Russia. On Monday, … Read more
In this article, we will look at an example of a complete machine learning (ML) pipeline using Python and PySpark. This pipeline includes data loading, preprocessing, feature engineering, model training, and evaluation. The main idea here is to provide you with a jump start on building your own ML pipelines. We will use Spark capabilities … Read more