AI & Data

Welcome to the How to Pick the Right LLM Learning Path! Designed for engineers and technical practitioners, this course moves beyond benchmark-driven defaults to build a repeatable approach to model selection, rooted in a core truth: there is no single best model, only the right model for a specific task. You will first master a framework built around six key selection factors: use case, performance, latency, cost at scale, deployment, and security. From there, the path dives into the distinct LLM call types that power agentic systems—including classification, planning, and tool dispatch—and explores how architectural properties like reasoning mode and structured-output compliance dictate fit. Finally, you will jump into a hands-on JupyterLab environment to benchmark models across four capability tiers on canonical agent tasks. By measuring real-world latency and token consumption, you will build a data-driven scorecard to confidently design optimized, multi-tier model architectures.