Generative AI Course

Week 4: Production and Deployment

Deploy robust GenAI systems with safety, observability, caching, and cost controls.

Duration: 5 Sessions

Labs: 6

Capstone: Enterprise GenAI App

DAY 1

Serving Architecture and APIs

FastAPI serving patterns and async inference
Request batching, streaming responses, and retries
Provider fallback and multi-model routing

DAY 2

Safety and Governance

Input moderation and output policy filters
Prompt injection defense and secrets handling
Human review queues for high-risk outputs

DAY 3

Observability and Evaluation in Production

Structured logging for prompts, context, and outputs
Quality dashboards and drift detection
Canary releases with automatic rollback triggers

json

{
  "trace_id": "req_10293",
  "model": "gpt-4.1-mini",
  "latency_ms": 1320,
  "token_in": 2100,
  "token_out": 420,
  "policy_flags": []
}

DAY 4

Cost, Performance, and Reliability

Caching and semantic reuse strategies
Prompt compression and response truncation policies
Timeout budgets and graceful degradation

Define per-endpoint token ceilings
Implement fallback model routing
Add cache for repetitive requests
Track cost per customer workflow

DAY 5

Capstone Demo Day

Lab 15: Deploy GenAI API with monitoring
Lab 16: Add moderation and safety middleware
Lab 17: Integrate RAG + vision + generation flow
Lab 18: Implement eval gates in CI
Lab 19: Add caching and fallback strategy
Lab 20: Present final production architecture

You can design and ship production-grade GenAI systems
You can evaluate quality, cost, and safety continuously
You have a capstone architecture ready for portfolio use

GUIDED PATH

Beginner Walkthrough: Ship a Real GenAI Product

What production really means (plain language)

Your app should not fail silently; it must return useful errors and fallback behavior.
Your app should be measurable; you must know response time, quality, and cost.
Your app should be safe; harmful prompts and risky outputs should be filtered.
Your app should be maintainable; another developer should understand and run it.

Daily launch plan (2 to 3 hours/day)

Day 1: Wrap your Week 3 assistant in a stable API with clear input/output contracts.
Day 2: Add safety middleware: prompt validation, output moderation, and secure secrets handling.
Day 3: Add tracing and logs: request id, latency, token usage, and model selection details.
Day 4: Add cost and reliability controls: cache repeated prompts and fallback to cheaper models when appropriate.
Day 5: Run final end-to-end tests, prepare demo, and publish your architecture summary.

Capstone requirements (must-have)

One API endpoint that accepts user question and optional context
At least one guarded path for unsafe or policy-violating inputs
Structured logs with trace id and token usage
At least one fallback model strategy
Automated test script for at least 15 prompts
Short runbook that explains how to deploy and monitor the app

Final acceptance checklist

Functionality: 15/15 core test prompts return usable responses
Safety: blocked prompts are handled with clear error messages
Latency: average response time remains within your target budget
Cost: token usage report included for at least 3 test scenarios
Operations: README + runbook allow another person to run the project

After this course: 30-day growth plan

Week 1: Improve UX and feedback loop with real users
Week 2: Add stronger retrieval and evaluation datasets
Week 3: Add role-based permissions and enterprise auth
Week 4: Package your project into portfolio case study with metrics

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