Animated mental models
Play, pause, replay and slow down request paths, service boundaries and inference timing.
AI Engineering · LLMOPS-AIP
LLMOps & AI Platform Engineering
Learn how organizations turn working AI demos into dependable services: serve models, trace requests, control access, measure quality, manage capacity and build reusable platform capabilities.
My Learning progress
0% complete
0 of 16 curriculum modules completed · 0/16 currently published modules complete · 0 with lab + knowledge-check evidence.
Learner promise
From “I can run a model” to “I understand how to operate an AI service.”
READ IT→SEE IT MOVE→HEAR IT→TRY IT→BREAK IT→DIAGNOSE IT→EXPLAIN IT
Learning experience
Visual first. Interactive. Voice assisted. Failure driven.
Difficult ideas are introduced with animated mental models, then reinforced with interactive simulators, a floating Learning Coach, multiple analogies, account-free labs, troubleshooting and production checklists.
Interactive simulators
Serving, routing, latency, GPU memory, reliability and trace simulators let learners change conditions and diagnose synthetic incidents.
Voice Learning Coach
The floating coach follows the visible section, explains it simply, gives alternative analogies, switches to engineer view and speaks through the browser voice engine.
Account-free core labs
The first labs use only Python 3. Optional Ollama exercises are clearly separated so beginners are never blocked by model downloads or API accounts.
Production Engineer checklist
Every module ends with a practical readiness checklist instead of vague completion percentages.
Complete production learning path
16 modules available · assessment and credentials enabled
All 16 learning modules, labs and the production capstone are present. This candidate now adds the gated formal assessment and separates the free completion credential from the stronger assessment-verified certificate.
Module 01
What Is LLMOps?
Understand why a model that works on a laptop is only the beginning of a production AI system.
Open module →Module 02Anatomy of an AI Application
Follow one request from the user's browser to the model runtime and back so every later platform concept has a place.
Open module →Module 03Running a Model Locally
Use Ollama as a friendly way to see the difference between a CLI, an API service, a model and the hardware doing the work.
Open module →Module 04Model Serving & Inference APIs
Understand what an inference server does and how synchronous and streaming responses change the user experience and operational metrics.
Open module →Module 05From Ollama to vLLM
Move from a friendly local model service to the ideas behind throughput-oriented production inference without treating either tool as universally better.
Open module →Module 06AI Gateways & Model Routing
Learn why organizations place a policy and routing boundary in front of heterogeneous model backends.
Open module →Module 07Tokens, Latency & Throughput
Learn the performance vocabulary that lets you distinguish a slow model from an overloaded service.
Open module →Module 08GPUs, Memory & Capacity
Build a practical memory mental model so 'the model fits' no longer means 'the service has enough capacity.'
Open module →Module 09Reliable AI Services
Learn how to keep one slow or failing model backend from turning into a system-wide retry storm.
Open module →Module 10Observing AI Systems
Learn how to follow one AI request across gateway, queue and inference instead of guessing from a single dashboard.
Open module →Module 11Evaluation & Quality Gates
Learn how to decide whether a model, prompt, retrieval or routing change is actually safe and useful to release.
Open module →Module 12Operating RAG & Agents
Operate retrieval and agent workflows by observing the separate stages instead of blaming every failure on the model.
Open module →Module 13AI Security & Governance
Protect AI applications from untrusted instructions, overpowered tools, sensitive-data exposure and uncontrolled resource use.
Open module →Module 14Kubernetes for AI Workloads
Understand how model-serving pods request accelerators, become schedulable, expose services and fail when resources or placement rules do not match.
Open module →Module 15Building an Internal AI Platform
Turn repeated model access, routing, evaluation, security and observability needs into a self-service platform that application teams can actually use.
Open module →Module 16Production AI Platform Capstone
Integrate serving, routing, capacity, reliability, observability, evaluation, security, Kubernetes and platform thinking in one incident-driven exercise.
Open module →Complete 16-module learning path
The difficulty rises gradually.
The difficulty rises deliberately: request path and serving first, then performance and reliability, then evaluation, RAG/agent operations, security, Kubernetes, platform engineering and a production control-room capstone.
What Is LLMOps?Available in this candidate
Anatomy of an AI ApplicationAvailable in this candidate
Running a Model LocallyAvailable in this candidate
Model Serving & Inference APIsAvailable in this candidate
From Ollama to vLLMAvailable in this candidate
AI Gateways & Model RoutingAvailable in this candidate
Tokens, Latency & ThroughputAvailable in this candidate
GPUs, Memory & CapacityAvailable in this candidate
Reliable AI ServicesAvailable in this candidate
Observing AI SystemsAvailable in this candidate
Evaluation & Quality GatesAvailable in this candidate
Operating RAG & AgentsAvailable in this candidate
AI Security & GovernanceAvailable in this candidate
Kubernetes for AI WorkloadsAvailable in this candidate
Building an Internal AI PlatformAvailable in this candidate
Production AI Platform CapstoneAvailable in this candidate
Learning modules
FREE
All 16 modules and visual simulations are open learning content.
Hands-on labs
FREE
16 account-free guided labs cover serving, routing, performance, reliability, evaluation, security, Kubernetes and the production capstone.
Assessment
FREE
30 randomized questions, 35-minute server timer, pass ≥75%, maximum 2 attempts per 24 hours, with at least 14 scenario questions.
Course completion certificate
FREE
After completing all modules, core labs and knowledge checks, request a publicly verifiable completion credential. It records learning completion, not an assessment score.
Request completion certificateAssessment-verified certificate
OPTIONAL · ₹499 incl. GST
Available only after passing the timed Academy assessment. It records assessment completion; it is not vendor accreditation or a professional licence.
Knowledge assessment
Operate the system from evidence.
The assessment is scenario-heavy and tests serving boundaries, routing, queueing, GPU capacity, reliability, observability, evaluation, RAG/agent operations, security, Kubernetes and platform operations.
The 84-question bank spans seven domains and contains 42 scenario/application questions. Each 30-question attempt preserves domain quotas and guarantees at least 14 scenarios.
16 MODULES→30 QUESTIONS · ≥14 SCENARIOS→35 MIN · PASS ≥75%→2 ATTEMPTS / 24H→OPTIONAL VERIFIED CERTIFICATE
Evidence policy
Completion and assessment remain distinct evidence.
My Learning and the private Learner Transcript track learning activity. Completing the curriculum can qualify for the free course-completion credential. Passing the timed assessment creates stronger assessment evidence and eligibility for the optional paid, publicly verifiable assessment certificate.