MLOps 2.0: Taming the LLM Lifecycle
1. Introduction to MLOps 2.0Traditional MLOps practices were designed around classical ML models: structured data, small artifacts, predictable behavior, and well-defined training pipelines. LLMs changed everything. Now you deal with: Massive mod...
The Rise of Small Language Models: A Practical Guide to Choosing SLMs Over Giants
If we’ve been following the AI space, it feels like the narrative has been dominated by one thing: bigger is better. We've watched parameter counts soar into the hundreds of billions, with each new model claiming to be more powerful than the las...
The AI-Powered DevOps Pipeline: Automating Code Reviews with Python and Node.js
Excellent concept! AI-powered DevOps pipelines are revolutionizing how teams ship code. Let me show you how to build a comprehensive automated code review system using Python for AI analysis and Node.js for pipeline integration.System Architecture Ov...
From Model to Production: Serving Your Python ML Model with a Node.js API
Why Node.js + Python for ML Serving?Node.js: Excellent for I/O-bound tasks, real-time APIs, and handling concurrent requestsPython: Unmatched for ML/Data Science workloadsTogether: Get the best of both worlds - Python's ML ecosystem with Node.js's pe...
Building a Real-Time AI Chatbot: Node.js for the WebSocket, Python for the Brain
The Core Concept: Why This Architecture?Node.js with WebSockets: Node.js is exceptionally good at handling many simultaneous, lightweight, and I/O-heavy connections. WebSockets provide a persistent, full-duplex communication channel between the...