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Intermediate CoursePart of AI Operator

AI Data, ROI & Model Selection

Move beyond static dashboards to autonomous business intelligence. Over three weeks, you will build an AI-driven analytics pipeline, deploy a Judge LLM to evaluate model outputs, and construct a dynamic ROI calculator that quantifies the exact financial case for every AI deployment.

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3 weeks

What You'll Learn

Build an automated insights pipeline that detects anomalies and emails leadership reports
Deploy a Judge LLM to score model outputs against accuracy and brand rubrics
Calculate total cost of ownership across API tokens, compute, and labor savings
Size models by balancing parameter count against cost and performance requirements
Draft an executive financial proposal with a dynamic ROI and payback calculator

Course Content

W1
Week 1: Autonomous Business Intelligence
Move past static dashboards to AI that anticipates executive needs.
1
The Shift from BI to Autonomous AI
Moving past static dashboards to AI that anticipates executive needs and surfaces insights before they are requested.
2
Raw Data Ingestion
Using advanced LLMs for rapid anomaly detection and automated data cleansing at the point of ingestion.
3
Semantic Parsing
Transitioning from reactive monitoring to proactive trend forecasting using semantic analysis of unstructured data.
4
Prompt Templates
Building cohesive, action-oriented reporting structures that translate raw data findings into leadership-ready narratives.
Weekly Win
Dashboard Deployment
Architect and deploy an automated, email-generating insights pipeline that delivers a live executive report from raw data.
W2
Week 2: AI Evaluation & "Judge LLMs"
Differentiate model capabilities from actual system performance.
1
The Eval-First Methodology
Differentiating model capabilities from actual system performance by building evaluation before building features.
2
Offline vs. Online Evaluation
Architecting separate environments for pre-deployment benchmarking and real-time production quality checks.
3
Evaluation Datasets
Structuring ground truth data and golden test sets for reliable, automated AI scoring.
4
Batch Testing & Rubrics
Designing strict scoring criteria covering accuracy, relevance, and brand alignment for batch model evaluation.
Weekly Win
Deploying a Judge LLM
Deploy a Judge LLM that automatically scores outputs from multiple models against a rubric of accuracy, relevance, and brand standards.
W3
Week 3: AI ROI & Cost Optimization
Quantify the exact financial case for every AI deployment.
1
Hard vs. Soft ROI
Categorizing direct financial impacts — cost reduction, revenue uplift — against long-term operational health metrics.
2
Total Cost of Ownership
Calculating complete deployment costs including API token usage, compute infrastructure, and measurable labor savings.
3
Model Sizing
Balancing heavy parameter models against efficient, low-cost alternatives to match task complexity with budget.
4
Optimization Calculator
Architecting dynamic formulas to compute precise ROI percentages, payback periods, and break-even timelines.
Weekly Win
Financial Proposal
Draft a complete executive proposal defining exact unit economics — cost per task, labor offset, and projected payback period — for a real AI deployment.

Prerequisites

Basic AI understanding
Understanding of business metrics

Tools You'll Use

ChatGPTChatGPT
ClaudeClaude
Google GeminiGoogle Gemini
Google SheetsGoogle Sheets
Power BIPower BI
📚
Intermediate Level
Course Price
₹6,999
India
$149
International · One-time payment
Next cohort starts Mar 30
Duration3 weeks
LevelIntermediate
FormatCohort-based
Modules3

What's included:

Live cohort sessions
Hands-on projects
Certificate of completion
Lifetime access
Career support

Part of Learning Track

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AI Operator
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