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Advanced CoursePart of AI Architect

AI Architect: Evaluation-First GenAIOps & GTM Strategy

Build the operational layer that keeps AI systems reliable, secure, and profitable. Master LLM evaluation pipelines, CI/CD for AI, the TRiSM security framework, distributed tracing with Langfuse, and go-to-market pricing strategy — then ship a production system with a complete economics dashboard.

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6 weeks

What You'll Learn

Design golden datasets and LLM-as-judge evaluation pipelines
Automate regression testing and artifact versioning in CI/CD
Apply the TRiSM framework to harden AI systems against adversarial inputs
Instrument LLM pipelines with Langfuse for end-to-end distributed tracing
Build token-based, subscription, and hybrid pricing models for AI products
Launch a production AI system with a full economics dashboard and GTM pitch

Course Content

W1
Week 1: Evaluation Frameworks
Measure what matters before you deploy what breaks.
1
The Evaluation Paradigm
Establish why evaluation is a first-class engineering discipline and how shipping without it creates invisible quality debt.
2
Component vs. End-to-End
Decide when to evaluate individual pipeline components in isolation versus running full end-to-end system evaluations.
3
Binary Scoring Systems
Design binary pass/fail rubrics for correctness, harmlessness, and groundedness that produce consistent, comparable evaluation signals.
4
The Golden Dataset
Curate a representative golden dataset that covers edge cases, adversarial inputs, and high-value user journeys for your specific domain.
5
Human Calibration Studies
Run inter-rater reliability studies to calibrate human evaluators and establish the ground truth that automated metrics must match.
Weekly Win
Golden Dataset and Scoring Rubric
A 100-example golden dataset with a binary scoring rubric validated against human evaluator agreement above 85%.
W2
Week 2: CI/CD for LLM Systems
Automate the quality gate so regressions never reach production.
1
CI/CD Fundamentals
Adapt software CI/CD principles to LLM pipelines: determinism challenges, eval budgets, and when to block versus warn on failures.
2
LLM-as-a-Judge Workflows
Use a strong LLM as an automated judge to score outputs at scale, with calibration checks to detect judge drift over time.
3
Regression Testing
Implement regression test suites that flag any model update degrading performance on golden dataset categories by more than a threshold.
4
Pipeline Automation
Wire evaluation into GitHub Actions or GitLab CI so every pull request triggers automatic eval runs before merge approval.
5
Artifact Versioning
Version prompts, model checkpoints, and evaluation results together so you can reproduce any historical system state exactly.
Weekly Win
Automated Evaluation CI Pipeline
A GitHub Actions workflow that runs the golden dataset evaluation on every PR and blocks merge if score drops below the defined threshold.
W3
Week 3: Security & Trust (TRiSM)
Harden your AI system against misuse, manipulation, and data exposure.
1
The TRiSM Framework
Apply Gartner's Trust, Risk, and Security Management framework to structure security controls across the AI system lifecycle.
2
Role-Based Access Control
Implement RBAC at the API gateway, model access, and output layer so users only interact with the AI capabilities they are authorized for.
3
Adversarial Resistance
Test and harden the system against prompt injection, jailbreaks, and indirect instruction attacks using red-teaming playbooks.
4
Data Masking
Detect and redact PII, secrets, and proprietary data from inputs and outputs before they reach external model APIs or logs.
5
Model Robustness Audits
Run structured robustness audits to measure performance degradation under distribution shift, adversarial perturbations, and OOD inputs.
Weekly Win
TRiSM Security Audit Report
A documented security audit covering RBAC configuration, adversarial test results, data masking validation, and remediation actions taken.
W4
Week 4: Observability & Tracing
See inside every LLM call so you can debug, optimize, and explain.
1
The Tracing Concept
Adapt distributed tracing concepts — spans, traces, and context propagation — to the unique structure of LLM pipeline observability.
2
Langfuse Integration
Instrument your LLM pipeline with Langfuse SDK decorators to capture traces, scores, and metadata without changing core logic.
3
Global Provider Conflicts
Resolve conflicts between LangChain's global callback provider and Langfuse's tracing context when running parallel pipelines.
4
Trace Attribute Mapping
Define custom trace attributes — session ID, user ID, feature flag, model version — and map them to Langfuse for filterable dashboards.
5
Telemetry Analysis
Analyze trace data to identify latency hotspots, token cost outliers, and error rate patterns across pipeline components.
Weekly Win
Instrumented LLM Pipeline
A fully traced LLM pipeline in Langfuse with custom attributes, cost tracking, and a latency breakdown dashboard across pipeline stages.
W5
Week 5: Pricing & Go-To-Market
Monetize your AI system in a way that scales with customer value.
1
The PLAN Framework
Structure your go-to-market using the PLAN framework: Positioning, Leverage, Acquisition, and Network effects specific to AI products.
2
Competitive Positioning
Map your AI product against incumbent and emerging competitors on axes of capability, latency, cost, and defensibility.
3
Token-Based Pricing
Design token-based pricing tiers that align cost to usage, including prepaid credits, overage rates, and model-tier pricing.
4
Subscription & Outcome Pricing
Structure subscription plans and outcome-based pricing models — pay-per-resolution, success fees — for enterprise AI contracts.
5
Hybrid Monetization
Combine subscription floors with usage-based overages and outcome bonuses into a hybrid pricing model that maximizes revenue across segments.
Weekly Win
Pricing Model and Positioning Deck
A pricing model document with competitive positioning, three subscription tiers, token overage rates, and a one-page GTM positioning statement.
W6
Week 6: Capstone — Production Rollout
Launch a fully governed, commercially viable AI system.
1
Capstone: CI/CD Construction
Wire the full CI/CD pipeline: eval on PR, artifact versioning, staging gate, and one-click production promotion with rollback.
2
Capstone: TRiSM Security Audit
Run the full TRiSM audit checklist on the production system, document findings, and close all critical and high-severity gaps.
3
Capstone: Economics Dashboard
Build a live economics dashboard showing cost-per-request, revenue-per-user, gross margin, and LLM spend breakdown by feature.
4
Capstone: Go-To-Market Pitch
Deliver a 10-slide GTM pitch covering positioning, ICP, pricing rationale, competitive moat, and a 90-day acquisition plan.
5
Capstone: Production Rollout
Execute a staged production rollout — 5% canary, 25% ramp, full release — monitored by the evaluation pipeline and Langfuse traces.
Weekly Win
Production AI System Launch
A fully rolled-out AI system with automated eval CI/CD, TRiSM audit sign-off, a live economics dashboard, and a GTM pitch delivered.

Prerequisites

LLM API and agent framework experience
Basic DevOps and CI/CD knowledge
Python proficiency

Hands-on Project

Deliver a production AI system with automated evaluation CI/CD, a TRiSM security audit, Langfuse tracing, an economics dashboard, and a go-to-market pitch deck.

📚
Advanced Level
Course Price
₹14,999
India
$249
International · One-time payment
Next cohort starts Mar 30
Duration6 weeks
LevelAdvanced
FormatCohort-based
Modules6

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 Architect
7 courses in track