In this article, I will be introducing a pipeline designed to identify sensitive data columns before masking steps and improve the efficiency of the data masking process.
We eliminated per-record Python-side Protobuf parsing and JVM-to-Python crossings by letting Flink's native Protobuf format decode records directly into typed columns.
A launch traffic spike hit cold-loaded LLM containers; shared-memory crashes and KV-cache OOMs taught us why GPU autoscaling needs warm floors, not reactive scaling.
Learn how to design production-ready agentic AI systems with human-in-the-loop workflows, approval gates, loop controls, and safe termination strategies.
Learn how to containerize a Python backtesting system with Docker, automate testing with GitHub Actions, and improve reproducibility through versioned builds.
Deploy a production-ready Spring Boot microservice on AWS Fargate with Docker, ECS, ALB health checks, private subnets, secrets, CI/CD, and autoscaling.
As Kubernetes deployments expand across hybrid and multicloud environments, permanently provisioned infrastructure becomes an expensive default. Here's how scale-from-zero aligns capacity with actual demand instead of worst-case scenarios.
Build reliable PySpark pipelines with techniques for data validation, schema evolution, transformation design, partition management, and operational monitoring at scale.
Legacy VMware on-prem, reactive AWS, and a ticket queue that never emptied — we deployed agentic AI across both substrates and changed how the team operates entirely.
Learn how to engineer production-ready AI agent context with a five-stage pipeline for retrieval, enrichment, verification, compression, and prompt injection.
Learn to build a strong testing pipeline to ensure code quality, as in enterprises today most code is generated by AI agents at a huge volume and scale.