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LLMOps Bootcamp

A focused 8-week bootcamp on operating LLM workloads — observability, evaluation, cost, safety, and incident response.

Duration

8 weeks · 6-8 hours/week

Level

Advanced

Delivery

Online

Status

Open for enrollment

Introduction

Why this technology matters.

LLMOps is the operations discipline for running LLM workloads reliably: observability, evaluation-gated releases, cost management, guardrails, and incident response. It matters now because the costliest failures are silent quality decay, token-bill spikes, and safety incidents that no one detects until users complain.

It is used to run gateways with quotas and fallbacks, CI pipelines that block bad releases, and runbooks for outages and drift in live assistants. It does not solve bad product design: observability does not fix irrelevant retrieval, guardrails do not fix hostile requirements, and dashboards do not fix unactioned alerts.

By the end the student will be able to build LLM observability across prompts, tools, and outputs, an evaluation-gated release pipeline with cost and safety checks, and an incident runbook with guardrails, rollback steps, and stakeholder reporting.

Why this course exists

The gap is between shipping a model endpoint and operating it through traffic spikes, drift, and incidents without losing trust or money. The bootcamp teaches the arc from Evaluation to Infrastructure to Security to Production, turning reactive firefighting into measured, gated operations.

Overview

Know exactly what you're signing up for.

Who is this for

DevOps practitionersPlatform engineersCloud engineersBackend developersML engineersAI engineers

Prerequisites

  • Experience operating cloud services and APIs
  • Familiarity with LLM applications
  • Comfort with monitoring and incident workflows

Technologies & tools

Observability platformsEvaluation harnessesCost dashboardsSafety guardrailsIncident runbooksCI pipelinesModel gateways

Skills you'll gain

LLM observabilityEvaluation gatingCost managementSafety controlsIncident responseRelease operations
Outcomes

What you'll be able to do.

  • Stand up an end-to-end LLMOps stack with tracing, evals, and budgets.
  • Run an incident response drill on a degraded LLM system.
  • Translate model behavior into operational SLOs your business can trust.
Projects

You will build.

Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.

  1. Project 01

    LLM observability setup

  2. Project 02

    Evaluation-gated release pipeline

  3. Project 03

    Cost control dashboard

  4. Project 04

    Safety incident response drill

  5. Capstone

    Operated LLM workload with observability and incident plan

Key concepts

Speak the language first.

LLM observability
Observability traces prompts, tool calls, and outputs so teams find slow or failing steps fast.
Evaluation-gated releases
Gated releases block deployment when eval scores drop, keeping bad changes out of production.
Cost management
Cost management attributes token spend by team and route, then caps or caches expensive paths.
Safety guardrails
Guardrails filter inputs and outputs for injection, leaks, and policy violations.
Incident response
Incident response defines roles, runbooks, and rollback steps for LLM outages and quality events.
Model gateways
Gateways route model traffic with keys, quotas, and fallbacks across providers.
CI pipelines for LLMs
CI pipelines run evals, cost checks, and safety scans automatically on every change.
Drift detection
Drift detection compares live outputs to baselines to flag slow quality decay.
Keep going

Fix, check, and go deeper.

Troubleshooting & common mistakes

Noisy alerts hide real LLM incidents

Tune thresholds on latency and error-rate signals and route quality alerts to a separate review queue.

Eval gate blocks every release

Inspect which cases fail and whether the golden set is stale, then update baselines before loosening gates.

Token bill spikes after a release

Attribute spend by endpoint and model on the dashboard, then restore caps and caching on the costly path.

Guardrail blocks legitimate traffic

Review blocked samples, narrow the rule patterns, and add an allowlist with audit logging.

Post-incident fixes never stick

Record the timeline in the runbook, add a regression eval for the failure, and assign an owner.

Before you move on, you should be able to

  • Deploy LLM observability across prompts, tools, and outputs
  • Build evaluation-gated release pipelines
  • Control cost with budgets, routing, and caching
  • Operate safety guardrails and audit trails
  • Lead incident response with runbooks and rollbacks
  • Report reliability, cost, and safety posture to stakeholders
Apply

Start your application.

Share a few details and a HIGAET advisor will reach out within one business day with next steps.

FAQ

Common questions

Ready to start LLMOps Bootcamp?

A 8 weeks course — Bootcamps.