Last year, I faced a critical decision: fine-tune our LLM or implement RAG? We chose fine-tuning. It was expensive, time-consuming, and didn’t solve our core problem. After building 20+ LLM applications, I’ve learned when to use each approach. Here’s the comprehensive decision framework that will save you months of work. Figure 1: Fine-Tuning vs RAG… Continue reading
Category: Emerging Technologies
Emerging technologies include a variety of technologies such as educational technology, information technology, nanotechnology, biotechnology, cognitive science, psychotechnology, robotics, and artificial intelligence.
CrewAI: Building Collaborative Multi-Agent Systems with Role-Playing AI Agents
Introduction: CrewAI has emerged as one of the most intuitive frameworks for building multi-agent AI systems. Unlike traditional agent frameworks that focus on single-agent loops, CrewAI introduces a role-playing paradigm where specialized AI agents collaborate as a “crew” to accomplish complex tasks. Released in late 2023 and rapidly gaining adoption throughout 2024, CrewAI simplifies the… Continue reading
Azure Event Hubs: A Solutions Architect’s Guide to Real-Time Data Streaming
Real-time data streaming has become essential for modern enterprises that need to process millions of events per second while maintaining low latency and high reliability. Azure Event Hubs stands as Microsoft’s fully managed, big data streaming platform, designed to handle massive throughput scenarios that traditional messaging systems simply cannot address. Having architected numerous streaming solutions… Continue reading
Mastering Hybrid Cloud with Google Anthos: Unified Kubernetes Management Across Any Environment
Introduction: Google Anthos provides a unified platform for managing applications across on-premises data centers, Google Cloud, and other cloud providers. This comprehensive guide explores Anthos’s enterprise capabilities, from GKE Enterprise and Config Management to Service Mesh and multi-cluster networking. After implementing hybrid cloud architectures for enterprises with complex compliance and data residency requirements, I’ve found… Continue reading
Azure Event Grid: A Solutions Architect’s Guide to Event-Driven Architecture
Event-driven architecture has become the backbone of modern distributed systems, enabling applications to respond to changes in real-time while maintaining loose coupling between components. Azure Event Grid represents Microsoft’s fully managed event routing service, designed to simplify the development of event-based applications at scale. After implementing Event Grid across numerous enterprise projects, I’ve gained deep… Continue reading
Semantic Kernel: Microsoft’s Enterprise SDK for Building AI-Powered Applications
Introduction: Semantic Kernel is Microsoft’s open-source SDK for integrating Large Language Models into applications. Originally developed to power Microsoft 365 Copilot, it has evolved into a comprehensive framework for building AI-powered applications with enterprise-grade features. Unlike other LLM frameworks that focus primarily on Python, Semantic Kernel provides first-class support for both C# and Python, making… Continue reading
Building Enterprise CI/CD Pipelines with Google Cloud Build: From Code to Production
Introduction: Google Cloud Build provides a fully managed CI/CD platform that executes builds on Google’s infrastructure with automatic scaling and pay-per-use pricing. This comprehensive guide explores Cloud Build’s enterprise capabilities, from multi-stage pipelines and artifact management to security scanning, approval workflows, and deployment automation. After implementing CI/CD pipelines for organizations deploying hundreds of times daily,… Continue reading
Azure API Management: A Solutions Architect’s Guide to Enterprise API Strategy
In my two decades of building enterprise systems, few architectural decisions have proven as consequential as API strategy. Azure API Management has evolved from a simple gateway into a comprehensive platform for managing the entire API lifecycle. Whether you’re exposing internal services to partners, building a developer ecosystem, or modernizing legacy systems through API facades,… Continue reading
Edge AI with ONNX Runtime: Running Models On-Device
Last year, I deployed an AI model to a mobile device. The first attempt failed—the model was too large, inference was too slow, and battery drain was unacceptable. After optimizing 15+ models for edge deployment using ONNX Runtime, I’ve learned what works. Here’s the complete guide to running AI models on-device with ONNX Runtime. Figure… Continue reading