After 20 years building data pipelines across multiple languages—Java, Scala, Go, Python—I’ve watched Python evolve from a scripting language to the undisputed standard for data engineering. This article explores why Python became the lingua franca of data pipelines and shares production patterns for building enterprise-grade systems. 1. The Evolution: From Java to Python In 2005, […]
Read more →Category: Technology Engineering
Technology Engineering
Building Cloud-Native Applications with .NET Aspire: A Comprehensive Guide to Distributed Development
Introduction: Building distributed applications has always been one of the most challenging aspects of modern software development. The complexity of service discovery, configuration management, health monitoring, and observability can overwhelm teams before they write a single line of business logic. .NET Aspire, Microsoft’s opinionated framework for cloud-native development, fundamentally changes this equation. After spending months […]
Read more →DIY LLMOps: Building Your Own AI Platform with Kubernetes and Open Source
Build a production-grade LLMOps platform using open source tools. Complete guide with Kubernetes deployments, GitHub Actions CI/CD, vLLM model serving, and Langfuse observability.
Read more →MLOps vs LLMOps: A Complete Guide to Operationalizing AI at Enterprise Scale
Understand the critical differences between MLOps and LLMOps. Learn prompt management, evaluation pipelines, cost tracking, and CI/CD patterns for LLM applications in production.
Read more →Agent Memory Patterns: Building Persistent Context for AI Agents
Introduction: Memory is what transforms a stateless LLM into a persistent, context-aware agent. Without memory, every interaction starts from scratch—the agent forgets previous conversations, learned preferences, and accumulated knowledge. But implementing memory for agents is more complex than simply storing chat history. You need short-term memory for the current task, long-term memory for persistent knowledge, […]
Read more →Tool Use Patterns: Building LLM Agents That Can Take Action
Introduction: Tool use transforms LLMs from text generators into capable agents that can search the web, query databases, execute code, and interact with APIs. But implementing tool use well is tricky—models hallucinate tool calls, pass invalid arguments, and struggle with multi-step tool chains. The difference between a demo and production system lies in robust tool […]
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