LLMOps & Production GenAI

Overview

LLMOps & Production GenAI focuses on what happens after an LLM application works on a laptop. You will work through an architecture review, a prompt…

What you will learn

  • Describe the LLMOps lifecycle and design a production reference architecture (Module 1).
  • Manage prompts and models as versioned assets with routing and fallbacks (Module 2).
  • Build evaluation datasets and automated quality gates in CI (Module 3).
  • Operate RAG data pipelines with index versioning and rollback (Module 4).

Prerequisites

  • Required: Python; experience building at least one LLM application; familiarity with cloud, containers, and CI/CD. Machine with Docker and access to a Kubernetes environment [confirm whether provided].
  • Helpful: Completion of Generative AI for Developers or RAG, LangChain & LangGraph. If you have not built an LLM app, start there.

Curriculum

  1. Module 1: LLMOps Foundations

    • Topics: What changes with generative models; the LLM application lifecycle; roles; gateway, orchestration, retrieval, and storage layers; build vs buy; multi-model strategies.
    • Deliverable: Architecture assessment. Completion check: Module 1 assessment.

    Hands-on:

    • Practice: Architecture Review. Gap list against the reference model.

  2. Module 2: Prompt and Model Management

    • Topics: Prompt registries; environments and rollouts; change review; benchmarks vs task evals; model gateways and fallbacks; open vs hosted models.
    • Deliverable: Registry and gateway config. Completion check: Module 2 assessment.

    Hands-on:

    • Practice: Prompt Registry and Gateway. Routing between two models with fallback.

  3. Module 3: Evaluation Engineering

    • Topics: Golden sets and synthetic data; labeling and rubrics; coverage; LLM-as-judge design; RAG metrics; regression gates in CI.
    • Deliverable: CI evaluation pipeline. Completion check: Module 3 assessment.

    Hands-on:

    • Practice: Eval Suite in CI. Pipeline that blocks regressions.

  4. Module 4: Data, Retrieval, and Pipelines in Production

    • Topics: Ingestion scheduling and freshness; chunking and embedding at scale; index versioning; retrieval monitoring; re-indexing; access control on data.
    • Deliverable: Pipeline code. Completion check: Module 4 assessment.

    Hands-on:

    • Practice: Versioned Index Pipeline. Versioning with rollback.

  5. Module 5: Observability and Monitoring

    • Topics: Spans, tokens, and costs; user feedback capture; dashboards; quality drift and hallucination rates; latency and error budgets; alerting.
    • Deliverable: Instrumented application. Completion check: Module 5 assessment.

    Hands-on:

    • Practice: Observability Stack. Tracing, cost tracking, and quality alerts.

  6. Module 6: Security, Privacy, and Governance

    • Topics: Prompt injection and jailbreaks; data leakage; supply chain and model risks; input and output guardrails; PII detection and redaction; audit logs.
    • Deliverable: Guardrail layer and test results. Completion check: Module 6 assessment.

    Hands-on:

    • Practice: Guardrail Layer. Guardrails plus a red-team suite.

  7. Module 7: Serving, Scaling, and Cost Optimization

    • Topics: Hosted APIs vs self-hosted inference; inference servers and quantization; batching and streaming; semantic caching; context compression; capacity planning.
    • Deliverable: Serving deployment. Completion check: Module 7 assessment.

    Hands-on:

    • Practice: Serve an Open Model. Inference server with semantic caching.

  8. Module 8: Fine-Tuning, Release, and Continuous Improvement

    • Topics: Prompting vs RAG vs fine-tuning; data preparation; evaluating tuned models; canary and A/B releases; feedback to data pipelines; incident response.
    • Deliverable: Release record. Completion check: Module 8 assessment.
    • Capstone (independent work, [CAPSTONE_HOURS] hours): Take an application through the full lifecycle: architecture, registry, CI evaluation gates, guardrails, observability, cost optimization, and a canary release with an operations runbook.

    Hands-on:

    • Practice: Canary Release. Release and roll back on metrics.

Course fee

$699 $399

What's included

  • Live cohort + recordings: Attend every session live and revisit the recordings for one year.
  • Real lab environment: Practise on a hands-on environment that mirrors real projects.
  • Live doubt-clearing support: Ask questions and get answers in real time during every session.
  • Assessments and lab activities: Apply what you learn through graded assessments and guided labs.
  • Learning assets: Keep the slides, notes, and reference material for good.
  • Role-readiness guidance: Mentor guidance to get you ready for the role you are targeting.
  • Verifiable certificate: Earn a shareable certificate on completing the hands-on capstone.
  • Global peer community: Learn with a global cohort and stay connected after the program.

In short

LLMOps & Production GenAI is a 24-hour instructor-led course covering Module 1: LLMOps Foundations, Module 2: Prompt and Model Management, Module 3: Evaluation Engineering. It includes guided practical labs.

Key facts

  • Course: LLMOps & Production GenAI
  • Duration: 24 hours
  • Modules: 8
  • Format: instructor-led live training

Frequently asked questions

Is this an advanced course?

Yes. You should have built an LLM application and be comfortable with containers and CI/CD. It is not an introduction to generative AI.

Do I need a GPU?

Module 7 serves an open model. Small models run on CPU; [confirm whether cloud GPU access is provided for larger models].

Which observability and evaluation tools are used?

An open or free-tier tracing tool and open-source evaluation libraries. Patterns transfer to commercial platforms.

Does this cover fine-tuning?

Module 8 covers when to fine-tune, data preparation, and evaluating tuned models. Full fine-tuning runs are demonstrated rather than run end to end by every learner [confirm].

How is this different from Generative AI for Developers?

That course builds the application. This course operates it in production with registries, gates, guardrails, monitoring, and releases.

Is Kubernetes required?

Serving labs use Docker and a local or provided Kubernetes environment. Prior Kubernetes exposure helps but is not required.

What do I keep?

A complete LLMOps reference: pipeline code, CI configuration, guardrails, dashboards, and a runbook template.

Is a corporate version available?

Yes, including an assessment of one of your own applications.

Topics covered

LLMOps & Production GenAI, Artificial intelligence, Corporate training