2025 marked a shift from simple prompts to complex AI systems. The year covered five learning paths: building autonomous agent systems with orchestration and structured outputs, mastering RAG pipelines with advanced retrieval and reranking, understanding time series forecasting with stationarity and Bayesian methods, revisiting fundamental ML algorithms with interpretability tools like SHAP, and focusing on data engineering fundamentals including EDA, handling imbalanced datasets, and A/B testing. The evolution moved from basic chatbots to production-ready agentic workflows with memory, tool use, and multi-step reasoning.
Table of contents
🗺️ Path 1: The Agent Architect🔍 Path 2: The RAG Specialist⏳ Path 3: The Time Series Analyst🧠 Path 4: The ML Practitioner (Back to Basics)🛠️ Path 5: Data Strategy, EDA & Engineering🚀 Looking Ahead to 2026Sort: