AI support analyst

Description

AI Support Analyst Syllabus (Beginner to Advanced)

 

Overview

Goal: learners become proficient in understanding, supporting, and troubleshooting AI-based products (chatbots, ML services, NLP pipelines, etc.), with a strong focus on analytics, communication, and tools.

Level 1: Foundations of Technical Support & AI Basics

Module 1: Role of an AI Support Analyst

• Overview of AI support roles
• Responsibilities: triaging, debugging, escalation, documentation
• Soft skills: communication, empathy, critical thinking

Module 2: Introduction to AI & Machine Learning

• What is AI? ML vs DL vs NLP
• Supervised vs unsupervised learning
• Overview of AI applications in customer support
• AI lifecycle: data → model → deployment → monitoring

Module 3: Technical Support Fundamentals

• Types of support: Tier 1, 2, 3
• Ticketing systems: Zendesk, Freshdesk, Jira
• SLA, escalation matrix
• Remote troubleshooting practices

Project Idea: Simulate customer ticket triage and escalation paths using sample chatbot issues.

---

Level 2: Technical & Analytical Skills

Module 4: Basics of Python for Support Analysts

• Python scripting for log parsing & diagnostics
• Reading JSON logs, REST API responses
• Working with error messages, exceptions

Module 5: APIs & Integrations

• What are REST APIs?
• Making GET, POST requests using requests
• Testing tools: Postman, Curl
• JSON structure & parsing

Module 6: Logs & Monitoring AI Systems

• Log formats (JSON, plain text, log levels)
• Tools: Kibana, Datadog (intro)
• Log-based troubleshooting strategies

Mini Project: Build a Python script to monitor API errors from logs and send alerts.

---

Level 3: AI Product Support Skills

Module 7: AI Chatbots & NLP Systems

• Understanding how chatbots work (intents, entities)
• Popular platforms: Dialogflow, Rasa, Watson, ChatGPT
• NLP pipelines: tokenization, intent classification

Module 8: AI Model Evaluation & Debugging

• Common issues: bias, hallucination, misclassification
• Metrics: accuracy, precision, recall, confusion matrix
• Debugging NLP/chatbot errors
• A/B testing AI responses

Project Idea: Analyze a chatbot’s performance logs and improve intent recognition accuracy.

---

Level 4: Deployment, Cloud & Automation

Module 9: Cloud Platforms & AI Services

• Intro to AWS/GCP/Azure AI tools (e.g., Amazon Lex, Azure Cognitive Services)
• Monitoring deployed models
• Accessing cloud-hosted logs (CloudWatch, Stackdriver)

Module 10: Automation & Scripting

• Automating ticket classification with AI
• Using Python to auto-respond based on logs
• Basics of shell scripting (Linux)

Project Idea: Create a support bot that fetches logs and suggests solutions using keyword matching.

---

Level 5: Data Analysis & Reporting

Module 11: Data Analysis for Support Insights

• Using SQL for support ticket analytics
• Creating reports with Pandas & Matplotlib
• KPI tracking: resolution time, error trends, CSAT impact

Module 12: Communication & Documentation

• Writing reproducible bug reports
• Creating knowledge base articles
• Customer communication strategies for AI issues

Tools & Technologies Covered

Category	Tools
Scripting	Python, Bash
Logs & Monitoring	Kibana, Datadog, CloudWatch
APIs	REST, Postman
Ticketing	Zendesk, Jira, Freshdesk
AI Platforms	ChatGPT, Rasa, Dialogflow, Hugging Face
Data	SQL, Pandas, Excel