Machine Learning & AI Best Practices¶

Objective: Master senior-level machine learning and AI patterns for production systems. When you need to build robust, scalable ML systems, when you want to follow proven methodologies, when you need enterprise-grade patterns—these best practices become your weapon of choice.

ML Systems Architecture¶

• ML Systems Architecture: Feature Stores, Model Serving, Experiment Governance, and Cross-System Reproducibility - Complete ML lifecycle architecture from ingestion to deployment with MLflow, ONNX, feature stores, and reproducibility standards

ML Operations¶

• Prompting LLMs - Best practices for effective LLM prompting with agentic instructions
• ONNX Model Optimization - Production-ready machine learning deployment with ONNX
• MCP + FastAPI Full Stack - Secure AI tool integration with Model Context Protocol
• Embeddings & Vector Databases - Production-grade semantic search and RAG systems
• Vibe → Agentic LLMs - From creative "vibe coding" to heavy prompts and managed, tool-using agents

Data Science¶

• R Data Exploration - Tidyverse vs data.table for production data analysis

These best practices provide the complete machinery for building production-ready ML and AI systems. Each guide includes architectural patterns, configuration examples, and real-world implementation strategies for enterprise deployment.