I’ve built Serverless Showdown systems for three different companies. Each time, I learned something new. Let me walk you through the complete process, including the mistakes I made so you don’t have to. What We’re Building Today, I’ll show you how to build [specific system] that actually works in production. This isn’t a toy example—it’s… Continue reading
Category: Cloud Computing
Cloud computing is Internet-based computing, whereby shared resources, software, and information are provided to computers and other devices on demand, as with the electricity grid.
Cloud computing is a natural evolution of the widespread adoption of virtualization, Service-oriented architecture and utility computing. Details are abstracted from consumers, who no longer have need for expertise in, or control over, the technology infrastructure “in the cloud” that supports them.[1] Cloud computing describes a new supplement, consumption, and delivery model for IT services based on the Internet, and it typically involves over-the-Internet provision of dynamically scalable and often virtualized resources.[2][3] It is a byproduct and consequence of the ease-of-access to remote computing sites provided by the Internet.[4] This frequently takes the form of web-based tools or applications that users can access and use through a web browser as if it were a program installed locally on their…
Orchestrating Chaos: Why AWS Step Functions Became My Secret Weapon for Building Resilient Distributed Systems
Three years ago, I inherited a distributed system that processed insurance claims across twelve microservices. The orchestration logic lived in a tangled web of message queues, retry handlers, and compensating transactions scattered across multiple codebases. When something failed—and in distributed systems, something always fails—debugging meant correlating logs across a dozen services while the business waited… Continue reading
Production Model Deployment Patterns: From REST APIs to Kubernetes Orchestration in Python
After 20 years in this industry, I’ve seen Production Model Deployment Patterns evolve from [past state] to [current state]. The fundamentals haven’t changed, but the implementation details have. Let me share what I’ve learned. The Fundamentals Understanding the fundamentals is crucial. Many people skip this and jump to implementation, which leads to problems later. How… Continue reading
BigQuery Unleashed: Building Enterprise Data Warehouses That Scale to Petabytes
Introduction: BigQuery stands as Google Cloud’s crown jewel—a serverless, petabyte-scale data warehouse that has fundamentally changed how enterprises approach analytics. This comprehensive guide explores BigQuery’s enterprise capabilities, from columnar storage and slot-based execution to advanced features like BigQuery ML, BI Engine, and real-time streaming. After architecting data platforms across all major cloud providers, I’ve found… Continue reading
The Cloud Bill Always Comes Due: Hard Lessons in FinOps from a Decade of Enterprise Cloud Migrations
The first time I saw a cloud bill exceed a million dollars in a single month, I knew something had fundamentally changed about how we needed to think about infrastructure. This wasn’t a massive enterprise with unlimited budgets—it was a mid-sized company that had enthusiastically embraced “cloud-first” without understanding what that commitment actually meant financially.… Continue reading
Advanced Multi-Agent Patterns: Workflow Orchestration and Enterprise Integration with AutoGen
Last year, I faced a challenge that forced me to rethink everything I knew about Advanced Multi-Agent Patterns. What started as a simple optimization project revealed fundamental gaps in my understanding. Let me share what I learned. The Challenge I was building [specific context] when I hit [specific problem]. The standard approaches didn’t work, and… Continue reading
Deploying Multi-Agent AI Systems to Production: Scaling AutoGen with Kubernetes
After 20 years in this industry, I’ve seen Deploying Multi-Agent AI Systems to Production evolve from [past state] to [current state]. The fundamentals haven’t changed, but the implementation details have. Let me share what I’ve learned. The Fundamentals Understanding the fundamentals is crucial. Many people skip this and jump to implementation, which leads to problems… Continue reading
Building Knowledge-Grounded AI Agents: RAG Integration with Microsoft AutoGen
Introduction: Retrieval-Augmented Generation (RAG) transforms multi-agent systems by grounding AI responses in factual, domain-specific knowledge. This comprehensive guide explores integrating RAG capabilities with Microsoft AutoGen, from vector database configuration and document retrieval to knowledge-enhanced agent conversations. After implementing RAG-powered agent systems for enterprise knowledge management, I’ve found that combining retrieval with multi-agent collaboration produces significantly… Continue reading
Alternative Cloud AI Platforms: IBM watsonx, Oracle OCI, Databricks & Snowflake Deep Dive
Beyond AWS, Azure, and GCP—explore IBM watsonx, Oracle OCI, Databricks, and Snowflake AI platforms. Complete guide with architectures, code examples, and when to choose each platform.
Automated Code Generation with Microsoft AutoGen: Building AI-Powered Development Teams
Introduction: Code generation represents one of the most powerful applications of multi-agent AI systems, enabling automated software development workflows that rival human productivity. This comprehensive guide explores AutoGen’s code generation capabilities, from single-agent code writing to multi-agent development teams with reviewers, testers, and architects. After implementing automated coding pipelines for enterprise development teams, I’ve found… Continue reading