Learning Roadmap
How to Become a AI Health Score Analyst
A step-by-step, phase-based learning path from beginner to job-ready AI Health Score Analyst. Estimated completion: 6 months across 4 phases.
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Foundations in Data & Customer Metrics
6 weeksGoals
- Master SQL for querying user interaction data.
- Learn core statistical concepts relevant to analysis.
- Understand key customer experience (CX) and product health metrics (e.g., CSAT, NPS, task completion).
Resources
- 'SQL for Data Analysis' (Udacity)
- 'Statistics for Business' (Coursera)
- Google Analytics Academy
MilestoneYou can independently pull and analyze customer interaction data from a database to report on basic usage and satisfaction trends.
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Core AI Evaluation & Analysis Toolkit
8 weeksGoals
- Learn Python for data analysis and scripting.
- Understand NLP basics and common evaluation methods for text.
- Get hands-on with LLM APIs (OpenAI, HuggingFace) to understand capabilities and failure modes.
Resources
- 'Python for Everybody' Specialization (Coursera)
- Hugging Face NLP Course
- OpenAI API Documentation & Examples
MilestoneYou can write Python scripts to process text data, call an LLM API, and perform basic sentiment analysis or classification on the outputs.
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Advanced Evaluation & Tooling Integration
6 weeksGoals
- Learn to use evaluation frameworks like 'langchain' evaluators or Hugging Face's 'evaluate' library.
- Understand experimental design for testing AI systems.
- Build automated monitoring pipelines.
Resources
- LangChain Evaluation Documentation
- Weights & Biases (W&B) Guides on Experiment Tracking
- Papers on LLM evaluation (e.g., HELM, BIG-bench)
MilestoneYou can design a comprehensive evaluation test for an AI chatbot, run it using an evaluation framework, and log the results systematically.
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Synthesis & Capstone Project
4 weeksGoals
- Integrate all skills into a single project: build a health score dashboard for a sample AI application.
- Develop storytelling skills to present findings.
- Study real-world case studies of AI system failures.
Resources
- Tableau Public tutorials
- Case studies from companies like Google PAIR, Microsoft Responsible AI
- Project: Analyze a public chatbot dataset.
MilestoneYou have a polished portfolio project demonstrating your ability to define, measure, monitor, and report on the health of an AI-powered experience system.
Practice Projects
Apply your skills with hands-on projects. Ordered by difficulty.
Customer Support Chatbot Health Dashboard
IntermediateBuild an end-to-end dashboard that ingests chatbot logs, calculates daily scores for accuracy (via keyword matching), sentiment (via a sentiment model), and user effort (via rephrase rate), and visualizes trends in a Grafana or Tableau dashboard.
LLM-as-a-Judge for Content Quality
AdvancedDevelop a pipeline where you use GPT-4 to evaluate the quality, safety, and helpfulness of a content generation model's outputs. Compare the LLM judge's scores to a human-rated sample to create a calibrated, scalable evaluation system.
RAG System Evaluation Benchmark
AdvancedCreate a benchmark suite for a Retrieval-Augmented Generation system. Include tests for retrieval relevance (using NLI), answer faithfulness, and end-to-end correctness. Use the Hugging Face `evaluate` library to run and track experiments.
Proactive Anomaly Detection for AI Logs
BeginnerWrite a Python script that monitors a stream of AI interaction logs (e.g., from a CSV or API) and flags anomalous conversations based on sudden drops in semantic similarity between user query and AI response, or spikes in user frustration keywords.
Ready to Start Your Journey?
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