Deployment & LLMOps
Sam Okafor
Sam covers what happens after the demo works: packaging, deployment, tracing, cost control and the operational habits that keep LLM services predictable.
Sample profileLoading page
Deployment
Deploying LLM Applications
Run LLM features in production with visibility, cost control, and security.
- Level
- Advanced
- Format
- Online · Self-paced
- Duration
- 8 weeks (sample schedule)
- Effort
- 5–7 hrs/week (suggested)
- Modules
- 6 modules
- Price
- Price on request (demo)
- Availability
- Open enrollment (demo)
Register interestEditable sample values
01
Overview
A prototype that works on your laptop is a long way from a service you can operate. LLM applications add their own operational concerns on top of the usual ones: variable latency, per-token costs, provider outages, and a new class of security risks.
This program covers the engineering needed to close that gap. You will serve an application with FastAPI, containerize it with Docker, instrument it with OpenTelemetry, and add caching, fallbacks, and budget limits. You will also work through prompt injection and data-leakage scenarios and the mitigations that reduce their impact.
The final project is an observability pipeline for an existing LLM service, with dashboards and alerts that tell you when quality, latency, or cost moves in the wrong direction.
fig. — request traceIllustrative
- POST /answer2380 ms
- embed_query
- vector_search
- rerank
- llm.generate1880 ms
- validate_output
0 ms2400 msTTFT 820 ms
02
Prerequisites
- Experience building and deploying a web service in Python
- Working knowledge of Docker and environment-based configuration
- Experience building at least one LLM-powered feature
- Familiarity with logging and basic monitoring concepts
03
Curriculum
Module 01: Serving LLM Applications
Expose LLM features through an API that handles streaming, errors, and concurrency.
- 01.1FastAPI service structure with Pydantic request and response models
- 01.2Streaming responses with server-sent events
- 01.3Async concurrency and connection management
- 01.4Timeouts, fallbacks, and graceful degradation
Module 02: Packaging and Configuration
Build reproducible deployments and keep secrets and prompts under control.
- 02.1Containerizing with Docker and multi-stage builds
- 02.2Managing secrets and environment configuration
- 02.3Versioning prompts and model settings as configuration
- 02.4Rolling out changes behind feature flags
Module 03: Latency and Cost Control
Keep response times and spend predictable as usage grows.
- 03.1Measuring time to first token and total latency
- 03.2Exact-match and semantic caching
- 03.3Routing requests between larger and smaller models
- 03.4Token budgets, rate limits, and per-user quotas
Module 04: Observability
See what your application did for every request and why.
- 04.1Structured logging for prompts, responses, and metadata
- 04.2Distributed tracing with OpenTelemetry
- 04.3Capturing token usage and cost per request
- 04.4Redacting sensitive data in logs and traces
Module 05: Security and Abuse Prevention
Understand the attack surface specific to LLM applications and reduce it.
- 05.1Direct and indirect prompt injection
- 05.2Data leakage through context and retrieved documents
- 05.3Output handling: treating model output as untrusted input
- 05.4Abuse monitoring and rate limiting
Module 06: Monitoring in Production
Detect drift and regressions after deployment and feed them back into development.
- 06.1Online quality signals and user feedback
- 06.2Dashboards and alerts for latency, errors, and cost
- 06.3Sampling production traffic for evaluation
- 06.4Incident review for LLM-specific failures
04
Skills and project outcomes
Skills you’ll practice
- Serving LLM features with FastAPI
- Containerized deployment with Docker
- Tracing with OpenTelemetry
- Caching and model routing for cost control
- Latency measurement and optimization
- Prompt injection mitigation
- Production monitoring and alerting
What you’ll build
- A containerized LLM service with streaming, timeouts, and fallbacks
- An observability pipeline capturing traces, token usage, and cost per request
- A caching and routing layer with measured latency and cost impact
- A security review documenting injection risks and mitigations
Related project briefs
Outcomes describe skills and artifacts you will practice. No certification, employment or career outcome is guaranteed.
05
Mentors
Evaluation & AI Reliability
Jordan Lee
Jordan teaches how to know whether an LLM feature is good enough to ship — and how to notice when it stops being good enough.
Sample profile06
Program FAQs
None in particular. Everything runs in Docker containers, and the deployment patterns apply to any platform that can run containers. Examples use open-source tooling where possible.
07
Where this fits
Interested in LLM Deployment?
Register your interest and tell us about your experience. We’ll suggest where to start.