SCHOOLOFCOREAI

LIVE · 12 WEEKS · PRODUCTION SYSTEMS

LLMOps Course

Build, deploy, evaluate, monitor and scale production LLM systems.

Learn the operational layer behind reliable LLM, RAG and agent applications—from serving and evaluation to observability, security, release controls and GPU cost management.

ServeEvaluateObserveScale
Explore CurriculumSee What You Build

PRODUCTION LLM SYSTEM

Production LLMOps System ArchitectureApplication → AI Gateway → Model/Fallback → Serving → Evaluation → Observability → ScalingAPPLICATIONRAG · Agents · APIsAI GATEWAYRoute · Cache · FallbackMODELvLLM · SGLangFALLBACKProvider APISERVINGBatching · KV-cacheEVALUATIONGolden-set · GatesOBSERVABILITYTraces · Cost · DriftTTFTEVALTRACEGPU
12 Weeks
Duration
6 production systems
Build
Code + Architecture
Reviews
Serving → AgentOps
Coverage
Certification
Included

Projects You Will Build in the LLMOps Course

Six production systems with real builds, measurable operations, and engineering artifacts submitted for review.

PROJECT 01

Multi-Model Inference Gateway

A concurrent inference endpoint with observable request flow and provider fallback.

Operate: TTFT · throughput · queue time · concurrency · fallback
vLLM · FastAPI · Kubernetes
Output: A load-tested serving system with a documented benchmark method.
REQUESTGATEWAYvLLM·PROVIDER

PROJECT 02

Production RAG Evaluation Pipeline

A retrieval pipeline with chunking, evaluation harness, and quality gates.

Operate: Recall@K · faithfulness · retrieval drift · citation accuracy
LangChain · LangSmith · Qdrant
Output: A RAG pipeline with eval gates and a retrieval quality report.

PROJECT 03

Fine-Tuning and Adapter Release Pipeline

A LoRA/QLoRA fine-tuning pipeline with experiment tracking and model registration.

Operate: Training metrics · adapter lineage · merge validation · promotion gate
MLflow · DeepSpeed · Hugging Face
Output: A fine-tuned adapter with MLflow lineage and deployment manifest.

PROJECT 04

LLM Observability Stack

A tracing and monitoring system with alert rules, drift detection, and cost tracking.

Operate: Latency · token cost · tool-call failures · drift signals
Langfuse · OpenTelemetry · Grafana
Output: An observability stack with alert configuration and a runbook.

PROJECT 05

Secure Agent System

A tool-using agent with allowlists, argument validation, and audit trails.

Operate: Tool-call failures · permission violations · trace latency · budget usage
LangGraph · FastAPI · Kubernetes
Output: A resilient agent with checkpoints, budgets, and approval gates.

PROJECT 06

Production Serving and Cost-Optimization System

A multi-model deployment with routing, caching, and GPU cost monitoring.

Operate: Cost per request · GPU utilization · cache hit rate · routing effectiveness
vLLM · SGLang · Ray Serve
Output: A cost-optimized deployment with a budget dashboard and capacity plan.

What Is LLMOps?

LLMOps is the engineering discipline for deploying, serving, evaluating, monitoring, securing and scaling LLM applications in production. It covers the operational layer around models, including prompt and model versioning, inference routing, latency and capacity management, quality gates, traces, retrieval and agent evaluation, guardrails, reliability and cost control. Unlike building a prototype with an API, LLMOps focuses on keeping LLM, RAG and agent systems measurable, dependable and economically viable after release.

PROTOTYPE → PRODUCTION

PromptVersioning and promotion
LLM APIRouting and fallback
RAGRetrieval evaluation
AgentTracing and controls
ModelServing and capacity
PrototypeReliability and cost

Building proves the feature works. LLMOps proves it keeps working under production conditions.

Why LLM Systems Fail in Production

See how production failures map to the signals engineers watch and the controls used to recover the system.

Serving + Capacity

BatchingAutoscalingRoutingCapacity PlanningServing Config

SIGNALS

TTFTTPOTThroughputQueue TimeConcurrent ReqsKV Cache

COURSE LAB

BenchmarkMeasureTuneRetest
INFERENCETTFT · TPOT · Throughput
CAPACITYQueue · Concurrency · KV Cache
QUALITYTask Success · Eval Score · Regression
RELIABILITYTimeout · Error · Fallback
ECONOMICSTokens/request · Cost/request · GPU
AGENTSTool Failures · Trace Latency · Loops

Who Should Take This LLMOps Course?

Your existing engineering skills are not discarded. LLMOps extends them into production AI operations.

YOU BRING

APIs

services

databases

authentication

LLMOPS ADDS

Serving

routing

evaluation

tracing

guardrails

YOU OPERATE

Production AI services with measurable quality and reliability

AI Platform EngineerInference EngineerML Platform Engineer

LLMOps Tools and Production Stack

A layered production system — not a list of tools. Select a layer to see what you study and build.

01 / 06

Application

The user-facing layer where LLMs solve real problems

You learn: How to build RAG, agent, and API features that are observable and controllable
You build: Production RAG and agent systems with evaluation and tracing
Tools: RAG · Agents · APIs

SYSTEM OUTPUT

Production RAG and agent systems with evaluation and tracing

12-Week LLMOps Course Learning Path

Five phases from foundations to production scale. Each phase has a starting state, a change, a build, and a checkpoint.

LLMOps Course Curriculum and Syllabus

15 learning blocks: 1 optional refresher, 13 core sections and 1 production capstone. Every module is server-rendered — expand any section to see topics, labs, and production artifacts.

Foundations

Engineering prerequisites and LLMOps fundamentals. 3 modules.

Containerized streaming FastAPI service3 modules

LLMOps reference architecture and model-selection decision matrix3 modules

Local-versus-cloud inference benchmark report3 modules

Build and Evaluate

Production APIs, PromptOps, evaluation, RAG and agents. 5 modules.

Production LLM gateway with routing, quotas, caching and fallbacks3 modules

Versioned prompt registry with evaluation and rollback workflow4 modules

Comparative evaluation dashboard and platform-selection report4 modules

Versioned production RAG pipeline with evaluation and re-index workflow4 modules

Resilient agent workflow with checkpoints, budgets and approval gates4 modules

Train and Scale

Fine-tuning operations and high-performance serving. 2 modules.

Fine-tuned adapter with MLflow experiment, dataset and deployment lineage4 modules

Scalable multi-GPU inference deployment with load-test and capacity report4 modules

Secure, Operate and Cloud

Security, governance, cost, reliability and cloud deployment. 5 modules.

LLM security test suite and red-team remediation report4 modules

Guardrail policy, approval workflow and governance evidence pack4 modules

Cost dashboard, capacity plan and production reliability runbook4 modules

AWS production reference architecture and deployment plan4 modules

Why Engineers Choose This LLMOps Course

Production Systems, Not Notebook Demos

Every project produces a deployable, observable, reviewed artifact — not a notebook with a model.predict() call.

Code and Architecture Reviews

Instructor reviews your code against production standards. Architecture reviews check failure boundaries and ownership contracts.

Evaluation-First Release Workflows

Every deployment passes through an evaluation gate. Quality regressions are caught before production, not after user complaints.

Vendor-Agnostic Infrastructure Decisions

Learn when to use vLLM vs SGLang vs managed endpoints, when to fine-tune vs prompt, when to cache vs route — not which vendor to memorize.

LLMOps Course vs Free Tutorials and Bootcamps

DimensionThis CourseFree Tutorials / Bootcamps
Production implementation6 deployable systems with reviewsVaries by provider — often notebook-only exercises
Evaluation workflowsGolden-set gates, LLM-as-judge, regression testsRarely covered in free tutorials
Code reviewInstructor PR-style review on every projectNo review in most free content
Architecture reviewFailure boundaries and ownership contracts checkedNot available in self-paced content
Live supportOffice hours, dedicated channel, debugging guidanceCommunity forums at best
CapstoneFull production LLMOps system with ops drillTypically absent

How the LLMOps Cohort Works

Live sessions, hands-on labs, structured reviews and practitioner feedback — the delivery model that turns curriculum into production capability.

Live Engineering Sessions and Hands-On Labs

Instructor-led live sessions on building production LLMOps infrastructure. Each project builds real serving, tracing, evaluation, and guardrails systems — not notebook demos.

Engineering Reviews and Practitioner Feedback

Every project goes through a structured review loop before it counts.

01BUILDLearner implements the production system
02SUBMITSubmit code, configs, and artifacts for review
03REVIEWInstructor reviews against production standards
04FIXLearner addresses review feedback
05RETESTVerify fixes pass evaluation gates

PR REVIEW

“Queue handling should be separated from inference logic. Move batching config to a dedicated module and add a health check.”

— Instructor code review feedback

EVALUATION GATE

PASS — Golden-set score 94.2% (threshold: 90%).

— Evaluation gate result

Ashutosh

AI Instructor

Reviews LLMOps serving, evaluation, and deployment artifacts.

Vivek Dwivedi

AIOps and Generative AI Instructor

Reviews LLMOps architecture, observability, and security artifacts.

Office Hours, Support and Recordings

Scheduled office hours for technical questions and debugging guidance. Session recordings and lab repositories remain accessible after the cohort.

LLMOps Learners and Career Outcomes

Working professionals who upskilled into production LLM operations — ML engineers, DevOps specialists, and AI infrastructure engineers building and scaling LLM systems.

Suel Abbasi

Suel Abbasi

Senior Data Scientist

I joined mainly to go deep into transformers and their variants. The specialization helped me understand LLM systems from the inside out — not just how to use them, but how to reason about and architect them.

GenAI Specialization
Prithvi

Prithvi

Marketer

I come from a marketing background and wanted to build AI products around growth. This course gave me the technical grounding to actually turn those ideas into working products.

GenAI Specialization
Ajit

Ajit

Lead Analytics

I was leading analytics teams and wanted to go deeper into NLP and neural networks. The specialization gave me the depth I was missing — I can now build and deploy AI solutions with real confidence.

GenAI Specialization
Praveen

Praveen

Senior Software Developer

I wanted to learn AI development and agentic AI — how to build and deploy intelligent applications end-to-end. The track gave me hands-on exposure to the full pipeline, from idea to production.

AI Developer
Deepak

Deepak

Senior Cybersecurity

I work in cybersecurity and wanted to upskill with agentic AI to build solutions in my own domain. The course helped me integrate AI into real security workflows — it was directly applicable to what I do.

Agentic AI
Vidula

Vidula

Senior Manager

I joined to learn the AIOps side of things — how ML systems actually run in production. The course gave me a solid grasp of operations, monitoring, and managing AI deployments at scale.

MLOps Specialization
Capstone ProjectsPeer ReviewsCareer SessionsPractice CommunitiesMock Interviews

School of Core AI Reviews

Honest feedback from working professionals who upskilled with SCAI — career switchers, senior engineers, and managers who chose to grow in AI.

Rated 4.9 / 5 by SCAI Learners

4.9 out of 5

Read School of Core AI reviews on Google — real experiences covering mentorship quality, project depth, placement support, and career transitions across AI, ML, and GenAI programs.

Read Reviews

LLMOps Certification and Assessment

What the LLMOps Certification Validates

The LLMOps Certificate validates that a learner can deploy, evaluate, monitor, secure, and scale production LLM systems.

LLM serving and inference deploymentEvaluation gate design and regression testingObservability stack setup and alert configurationSecurity guardrails and tool access controlCost monitoring and GPU optimizationCI/CD pipeline design with evaluation gates

How You Earn the LLMOps Certification

  • · Complete all 6 production system builds
  • · Submit each build for code and architecture review
  • · Pass the capstone production LLMOps system review
  • · Demonstrate operational skills: serving, evaluation, observability, and rollback

Review method: Instructor code review with inline comments on production readiness. Architecture review against operational standards.

Revisions and resubmissions are allowed after review feedback.

What the Certificate Represents

Certificate includes the learner name, completion date, and capstone project reference.

The certificate is a completion certificate from School of Core AI, not an industry accreditation or external certification.

LLMOps Course Fees and What’s Included

LLMOps Course

₹35,000

One-time payment

Duration:
12 Weeks
Format:
Live Online Cohort
Delivery:
Live sessions + recordings + code review
Start Enrollment

WHAT’S INCLUDED

  • · 6 production system builds with real infrastructure
  • · Capstone production LLMOps system
  • · Code and architecture reviews on every project
  • · Evaluation gate passage with golden-set tests
  • · Live sessions, office hours, and technical support
  • · Session recordings and lab repository access
  • · LLMOps completion certificate with capstone reference
Payment: One-time payment. Talk to the team about installments or corporate/team training.

How to Enroll in the LLMOps Course

Check the Prerequisites

Basic Python programming and familiarity with LLM or ML fundamentals. Prior DevOps or MLOps experience is helpful but not mandatory.

Talk to the LLMOps team about course fit, curriculum details, batch schedule, fees, installments, and corporate or team training options.

WhatsApp the Team

Talk to the LLMOps Team

Fill this out and we’ll get back to you.

After your conversation, you’ll receive a registration link and batch details to confirm your seat.

Which Production-AI Track Fits Your Career Path?

Not sure if LLMOps is the right starting point? Use this pathway to understand how MLOps, LLMOps, and AIOps relate to each other and which course matches your career goals.

STEP 01

MLOps Foundations

Start with ML pipelines, experiment tracking, model deployment, and drift monitoring. Best for engineers building traditional ML systems.

Explore MLOps Course →

STEP 02 · YOU ARE HERE

LLMOps Specialization

Serve, evaluate, observe, secure, and optimize LLM, RAG, and agent systems in production. Best for engineers working with LLM applications.

You are here — Start Enrollment →

STEP 03

AIOps Broad Coverage

A broader program spanning production ML, LLM systems, RAG, agents, and AI-platform operations. Best for engineers needing end-to-end AI operations.

Explore AIOps Course →

Still deciding? Here is a quick guide:

Stay with this LLMOps course if

Your primary goal is to serve, evaluate, observe, secure, and optimize LLM, RAG, and agent systems in production.

Choose MLOps course if

Your primary goal is traditional ML data and model pipelines, experiment tracking, model deployment, drift monitoring, and retraining.

Choose AIOps course if

You need a broader program spanning production ML, LLM systems, RAG, agents, and AI-platform operations.

Frequently Asked Questions About the LLMOps Course

What is LLMOps?

LLMOps is the engineering discipline for deploying, serving, evaluating, monitoring, securing, and scaling LLM applications in production. It covers inference routing, latency management, quality gates, traces, retrieval and agent evaluation, guardrails, reliability, and cost control. Unlike building a prototype with an API, LLMOps focuses on keeping LLM, RAG, and agent systems measurable, dependable, and economically viable after release.

How is LLMOps different from MLOps?

MLOps covers the production lifecycle for traditional ML systems — data pipelines, training, registry, serving, and monitoring. LLMOps covers the production lifecycle for LLM systems — real-time inference with token streaming, prompt versioning, retrieval evaluation, agent tracing, guardrails, and token-level cost control. Both share CI/CD and monitoring principles, but LLMOps adds LLM-specific concerns like context windows, KV-cache, prompt regression, and injection defense.

What does an LLMOps engineer work on?

An LLMOps engineer works on inference serving and optimization, evaluation gates and regression testing, observability and tracing, routing and fallback design, prompt and model lifecycle management, RAG operations, agent controls, security guardrails, and GPU cost optimization. The role bridges ML, backend engineering, and platform operations with a focus on keeping LLM systems reliable and measurable in production.

Does LLMOps include LLM deployment and serving?

Yes. LLM deployment and serving are core LLMOps capabilities. You learn to deploy models behind production APIs using vLLM and SGLang, configure continuous batching and KV-cache management, measure latency under concurrent traffic, and optimize GPU utilization. The course covers serving architecture, load testing, and capacity planning as production engineering skills.

Does this LLMOps course cover monitoring and evaluation?

Yes. Monitoring and evaluation are central to the curriculum. You build observability stacks with Langfuse and OpenTelemetry, set up trace-level monitoring for latency, cost, and tool calls, and create evaluation gates using golden-set datasets and LLM-as-judge. The course teaches you to detect quality regressions, drift, and cost issues before they affect users.

Does LLMOps include RAGOps and AgentOps?

Yes. RAGOps covers operating retrieval pipelines with versioning, incremental ingestion, re-indexing, freshness SLAs, and retrieval drift monitoring. AgentOps covers building bounded agent workflows with tool allowlists, argument validation, token budgets, circuit breakers, and step-level tracing. Both are production operations disciplines that extend beyond building a RAG prototype or a simple agent.

Is this LLMOps course suitable for backend engineers?

Yes. Backend engineers are well-positioned for LLMOps because they already understand APIs, services, databases, and authentication. The course adds LLM-specific serving, routing, evaluation, tracing, and guardrails on top of existing engineering skills. Backend engineers typically move fastest through the serving and CI/CD modules and add the most value in evaluation and observability.

Do I need Kubernetes before learning LLMOps?

No. The course includes a refresher module on Docker and Kubernetes basics. You need basic Python programming and familiarity with LLM or ML fundamentals. Prior DevOps or containerization experience is helpful but not mandatory. The curriculum builds the infrastructure skills alongside the LLMOps-specific content.

What does the LLMOps certification validate?

The certificate validates that you can deploy, evaluate, monitor, secure, and scale production LLM systems. It requires completing all 6 production builds, passing code and architecture reviews, and delivering a capstone production system. The certificate is a completion certificate from School of Core AI, not an industry accreditation or external certification.

How long does it take to learn LLMOps?

The structured program runs for 12 weeks as a live online cohort. The timeline assumes basic Python and ML familiarity. Engineers with existing DevOps or MLOps experience may move faster through the infrastructure modules. The 12-week duration includes live sessions, hands-on labs, code reviews, and the capstone project.

Is the LLMOps course live and online?

Yes. The LLMOps course is a 12-week live online cohort. Sessions are instructor-led, with recordings, code review, office hours, and lab repository access. The live format allows real-time debugging guidance and architecture feedback on your production builds.

What is the LLMOps course fee and what is included?

The course fee is a one-time payment of 35,000 INR. It includes all live sessions, six production system builds, code and architecture reviews, evaluation gate passage, office hours, technical support, session recordings, lab repository access, and the LLMOps completion certificate with capstone review.

Ready to build production LLM systems?

Explore the next cohort or talk to the team.

Start EnrollmentTalk to an Advisor

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