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AI & ML Evaluation Roadmap 2026

Complete Guide

A structured, practical roadmap for measuring the quality of AI and machine learning systems. Ten parts walk you from evaluation fundamentals and classic ML metrics, through LLM, RAG, and agent evaluation, to safety checks, production monitoring, evaluation infrastructure, and hands-on projects. Every chapter includes explanations, formulas, code examples, tables, and exercises — all written in plain language.

Table of Contents

• 1. Evaluation Fundamentals What evaluation is, metrics vs objectives, reliability, statistical testing, experimental design, golden datasets
• 2. Machine Learning Evaluation Classification, regression, ranking & retrieval metrics, error analysis, slices, robustness
• 3. LLM Evaluation Human review, LLM-as-a-judge, pairwise comparison, rubrics, hallucination, faithfulness, benchmarks
• 4. RAG Evaluation Retrieval quality, chunking, embeddings, rerankers, context precision/recall, citations, RAGAS, DeepEval, TruLens
• 5. AI Agent Evaluation Task success, planning, tool use, memory, multi-turn, function calling, multi-agent, benchmarks
• 6. Safety Evaluation Toxicity, bias, prompt injection, jailbreaks, red teaming, privacy, governance standards
• 7. Evaluation Frameworks OpenAI Evals, DeepEval, Promptfoo, LangSmith, Arize Phoenix, MLflow, Hugging Face, lm-evaluation-harness
• 8. Production Evaluation Offline vs online, A/B testing, shadow deployment, drift, latency & cost, monitoring
• 9. Evaluation Infrastructure Pipelines, dataset versioning, experiment tracking, batch & distributed evaluation, dashboards, leaderboards
• 10. Practical Projects Metric library, judge system, RAG pipeline, agent evaluator, safety suite, CI/CD, production dashboard