LLMOps
Develop the skills to design an observable release process for an LLM application through a learning path centred on LLMOps. Explore concepts, guided practice and an applied project.
Build practical LLMOps skills.
Skills and knowledge developed in this course.
- Application versioning
- Evaluation gates
- Production visibility
- Operational response
- Applied project: Design an observable release process for an LLM application
- LLMOps — validation and review
About this course
This course develops practical capability in LLMOps. Its course-specific content covers Application versioning, Evaluation gates, Production visibility, Operational response, followed by an applied project focused on design an observable release process for an llm application. The lessons include concrete configuration, design, testing or operational decisions appropriate to the subject.
Who this is for
Administrators, developers and practitioners who work with LLMOps or plan to take on responsibilities in this area.
Before you start
Basic computer literacy and a foundational understanding of artificial intelligence & generative ai. Prior experience with the named product helps with the practical exercises.
Design an observable release process for an LLM application
Deliver design an observable release process for an llm application. Use the module outcomes to state requirements, demonstrate a working result, test an expected failure or boundary case, and document assumptions and follow-up work.
Course content
Course-specific syllabus and practical exercises. Lab release, access arrangements and final delivery scope are confirmed before enrolment.
01Application versioning
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- Prompts, models and retrieval snapshots
- Dataset provenance and environment configuration
Practical exerciseApply application versioning to the design an observable release process for an llm application and capture the validation evidence.
02Evaluation gates
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- Offline benchmarks and regression thresholds
- Human review and release acceptance
Practical exerciseApply evaluation gates to the design an observable release process for an llm application and capture the validation evidence.
03Production visibility
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- Token cost, latency and tool traces
- Feedback collection and failure classification
Practical exerciseApply production visibility to the design an observable release process for an llm application and capture the validation evidence.
04Operational response
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- Canary releases and fallback models
- Incident handling, access reviews and rollback
Practical exerciseApply operational response to the design an observable release process for an llm application and capture the validation evidence.
05Applied project: Design an observable release process for an LLM application
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- Define inputs, assumptions and acceptance criteria for: Design an observable release process for an LLM application.
- Implement the project using application versioning, evaluation gates, production visibility, operational response.
Practical exerciseDesign an observable release process for an LLM application.
06LLMOps — validation and review
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- Demonstrate the completed project against its acceptance criteria.
- Exercise an invalid-input or failure scenario and document the observed result.
Practical exercisepresent the project evidence, limitations and a repeatable handover.
Technical references
These primary vendor, project or standards sources inform this independent IT Purview outline. Reference pages checked 25 October 2026; current provider documentation and exam objectives take precedence when versions change.
From understanding to application.
Progress by learning milestone. The schedule is agreed around your starting level and learning goals.
- 01Learning milestone 1 In progress
Set the context
Application versioning · Evaluation gates
Establish the LLMOps scope, baseline concepts and operating assumptions. - 02Learning milestone 2 In progress
Build the working skills
Production visibility · Operational response
Apply the core LLMOps techniques in the course lab and record the result. - 03Learning milestone 3 In progress
Validate and handle exceptions
Check expected behavior, investigate a failure case and capture evidence for design an observable release process for an llm application. - 04Learning milestone 4 In progress
Deliver the applied project
Complete design an observable release process for an llm application, explain key decisions and identify a practical next step.
Career outlook after this learning path
Typical roles this roadmap can support, with broad salary ranges by relevant work experience.
| Role | 0–3 years | 3–7 years | 7–12+ years |
|---|---|---|---|
| AI / ML Engineer | ₹10–17 LPA | ₹20–35 LPA | ₹38–62 LPA |
Ranges are indicative India-market compensation estimates in ₹ lakh per annum, not a salary promise. They reflect broad market guides and vary by location, employer, skills and role scope; the experience bands refer to relevant work experience, not time since training. Completing this course does not guarantee employment, a role, compensation or career progression. View market reference ↗