OneRange

LLMOps & Production Deployment

This course teaches ML and platform engineers to take AI features from prototype to reliable production. It covers deployment patterns, monitoring and logging, versioning, guardrails, and incident response. Learners finish able to run AI in production with confidence.

What this course covers

The course runs across 10 topics, each one a short adaptive session rather than a recorded lecture. The tutor explains the idea, works an example, checks understanding, and adjusts the next step to the answer given.

  • Production Deployment Patterns for LLMs — Deploying LLM applications using blue-green, canary, and shadow deployment strategies. Configuring routing layers to manage traffic distribution between model versions.
  • Hands-On LLM Deployment — Packaging an LLM application into a container and deploying it to a staging cluster, setting up auto-scaling properties based on concurrency and token throughput.
  • LLM Telemetry: Monitoring and Logging — Instrumenting LLM applications to track key operational metrics (latency, token usage, cost) and quality metrics (semantic drift, prompt injection attempts).
  • Configuring Real-Time Alerts and Dashboards — Setting up centralized logging pipelines and configuring real-time alert thresholds for anomalous spikes in latency, error rates, or cost metrics.
  • Model and Prompt Versioning Control — Implementing semantic versioning for prompts, system instructions, and weight checkpoints. Integrating model registries with CI/CD deployment pipelines.
  • Implementing LLM Guardrails — Configuring active input/output guardrail frameworks (e.g., NeMo Guardrails, Llama Guard) to filter toxic content, prevent jailbreaks, and enforce PII masking.
  • Testing and Validating Guardrail Rules — Writing test suites to stress-test deployed guardrails against adversarial prompts and evaluating the latency overhead introduced by the guardrail layer.
  • Incident Response: Triaging LLM Outages — Simulating common production failures such as rate-limit exhaustion (HTTP 429), model hallucinations, and cascading API dependencies.
  • Implementing Fallbacks and Redundancy — Coding programmatic fallbacks, circuit breakers, and multi-provider failover routing logic to maintain high availability during primary model outages.
  • Post-Mortem and Continuous Improvement — Conducting post-incident analysis on LLM failures, isolating bad system prompts, and updating evaluation datasets to prevent recurrence.

Skills you build

Results are measured against named skills in the OneRange taxonomy of more than 10,000 skills, so a manager sees proficiency per skill rather than a completion tick. This course maps to Model Monitoring & Maintenance, Platform Engineering, LLM Operations (LLMOps), Machine Learning Operations, Model Versioning & Experiment Tracking.

  • Model Monitoring & Maintenance
  • Platform Engineering
  • LLM Operations (LLMOps)
  • Machine Learning Operations
  • Model Versioning & Experiment Tracking

Who it is for

ML engineers, platform teams. The material is pitched at expert level, and takes roughly 150 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

It sits in the Category: Deployment track of the Vero AI Catalog, and can be assigned to one person, a team, or the whole company.

How it is delivered and assessed

Delivery is conversational and interactive, including guided lab, code lab, branching scenario exercises. There is no video to sit through and no slide deck to click past.

Understanding is checked with a code assessment of 5 items, with a pass mark of 70%.

Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Also in this track

Related

Frequently asked questions

How long does this course take?

It runs roughly 150 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

How is it assigned to a team?

An administrator assigns it from the Vero dashboard to one person, a team, a department or the whole company, and sees progress and results per person and per skill.

Can we customise it with our own documents and terminology?

Yes. Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Course code CAT-MLOPS.