What Is AIOps and Why Is It Important in 2026?

AIOps (AI Operations) is the discipline of engineering reliable, observable, and scalable AI systems in production. It unifies MLOps for model lifecycle, LLMOps for LLM serving and prompt management, and AgentOps for autonomous agent orchestration into a single operational framework. In 2026, with enterprises deploying AI at scale, AIOps engineers are critical for ensuring production AI systems meet SLAs, control costs, and comply with governance standards.

How Does AIOps Differ from MLOps and LLMOps?

MLOps handles model training pipelines, versioning, and deployment. LLMOps extends that to large language model serving, prompt management, and inference optimization. AIOps is the umbrella discipline — it unifies MLOps, LLMOps, and AgentOps into a single operational framework covering the full lifecycle of production AI systems: pipelines, serving, orchestration, observability, drift detection, and governance.

What Level of Experience Is Expected Before Enrolling?

This is not a beginner course. You should be a working professional — AI engineer, data scientist, MLOps engineer, DevOps or SRE, or technical lead — with hands-on Python experience and exposure to ML or data infrastructure. The curriculum assumes you have deployed code to production before and understand basic cloud and container concepts.

What Is the Course Duration and Weekly Commitment?

The course runs six months with live mentor-led sessions. Each month covers one deep-dive phase — from MLOps foundations through LLM serving, agent orchestration, observability, and a production capstone. Expect approximately 15 to 20 hours per week across lectures, labs, and project work.

What Production Systems Will I Build, and Can I Use Them in My Portfolio?

You build eight production systems: an observability pipeline, a drift detection system, high-performance LLM serving, an agent orchestration platform, a production RAG pipeline, a cost analytics dashboard, CI/CD with eval gates, and a governance framework. Your capstone integrates these into one AIOps platform. All of them are yours to showcase in your portfolio.

What Certification Do I Receive, and Does Placement Support Come with It?

You earn an AIOps Professional Certificate issued by School of Core AI upon completing the capstone and completion requirements. Placement support includes resume building around your deployed projects, mock interviews with practising engineers, career coaching, and placement assistance. Your capstone system becomes your strongest interview artifact.

What Tools and Technologies Are Covered in This AIOps Course?

The course covers MLflow for experiment tracking, vLLM and TGI for LLM inference, LangSmith and Langfuse for observability, LangGraph and CrewAI for agent orchestration, DVC for data versioning, LlamaIndex for RAG pipelines, Docker and Kubernetes for containerization, Grafana and Prometheus for monitoring, and OpenTelemetry for distributed tracing.

What Is the AIOps Course Fee, and Are EMI Options Available?

The AIOps course fee is ₹80,000 INR as a one-time payment. This covers live mentorship, production project access, capstone review, and placement support. EMI options are available — contact the admissions team for flexible payment plans. Cloud, GPU, and third-party subscriptions needed for your own project work are not included in the fee.

Is This AIOps Course Available Online? Can I Attend from Anywhere in India?

Yes, the AIOps course is fully online with live mentor-led sessions. You can attend from anywhere in India or internationally. All sessions are recorded for catch-up, and you get access to a dedicated cohort workspace with peer collaboration channels.

What Career Roles Can I Pursue After Completing This AIOps Certification?

Graduates are prepared for roles including AIOps Engineer, MLOps Engineer, LLMOps Engineer, AI Platform Engineer, AI Infrastructure Engineer, ML Production Engineer, and AI DevOps Specialist. With the hands-on capstone and production systems in your portfolio, you will be positioned for mid-to-senior level positions in AI operations.

Can I Learn AIOps Through Free Courses?

Free courses can introduce individual tools like MLflow or vLLM, but they rarely cover the connected production stack — serving, observability, drift detection, governance, and incident response — in one coherent workflow. This course builds all eight production systems with mentor reviews and operational drills so you learn how the layers work together in real deployments, which is difficult to replicate from free, fragmented content.

LIVE ONLINEINDIA

AIOps Course in India

Build every layer of the AIOps stack—ML systems, distributed training, LLM serving, RAG systems, and autonomous agents—from scratch.

Many courses teach isolated tools and call it a curriculum. This AIOps course teaches how the full system fits together. You will design ML systems and reproducible pipelines, fine-tune LLMs on multi-GPU infrastructure, build high-throughput inference servers, architect evaluated and monitored RAG pipelines, and ship autonomous agents—with Kubernetes orchestration, end-to-end observability, and security built in. From local development to cloud deployment, every stage is covered.

06MONTHS

Live Cohort

08SYSTEMS

Built End-to-End

4–5HRS

Per Week

₹80KEMI

Available

Download Syllabus

Live cohort · Mentor-led · Placement support after completion.

AIOPS / LIVE SYSTEMOnline

Next Cohort

SEP 2026

Format

Live Online

Duration

6 Months

Enrolment

OPEN

From Building AI → Operating AI

DEFINITION/AIOPS / 01

What Is AIOps? MLOps, LLMOps & AgentOps Explained

AIOps (AI Operations) is the discipline of engineering reliable, observable, and scalable AI systems in production. It unifies MLOps for the model lifecycle, LLMOps for LLM serving and prompt management, and AgentOps for autonomous-agent orchestration—covering monitoring, drift detection, inference optimization, cost control, security, and governance across the AI lifecycle.

For modern AI teams, AIOps connects research with production infrastructure so AI systems operate reliably under real-world conditions. Within LLMOps, RAGOps covers the retrieval infrastructure, indexing, evaluation, and monitoring required for dependable production RAG systems.

AIOPS / OPERATING MODELActive

01 / MODEL SYSTEMS

MLOps

Operate the complete machine-learning lifecycle from data and training to deployment, monitoring and retraining.

DATATRAINREGISTERSERVEMONITOR

02 / LANGUAGE SYSTEMS

LLMOps

Operate large language models with prompt management, evaluation, scalable serving, tracing and inference optimization.

PROMPTEVALUATESERVETRACEOPTIMIZE

03 / AUTONOMOUS SYSTEMS

AgentOps

Operate agent workflows across orchestration, tools, memory, runtime tracing, evaluation and guardrails.

PLANORCHESTRATEACTTRACEGOVERN

AIOPS / Shared Production Layer

OBSERVABILITY

RELIABILITY

DRIFT DETECTION

EVALUATION

COST CONTROL

GOVERNANCE

The Stack/Layer Progression

The AIOps Course Stack — From ML Pipelines to Autonomous Agents

AIOps spans three connected layers — the classical ML foundation, the LLM and RAG layer on top of it, and the agent systems that orchestrate everything. Each builds on the one before it.

01Model Systems

MLOps — Model Lifecycle & Pipelines

Data versioning, experiment tracking, model registry, CI/CD for model deployments, and automated retraining pipelines. The foundation every production AI system is built on.

Data pipelines & versioningExperiment tracking & model registryCI/CD & automated retraining

MLOps course

02Language Systems

LLMOps — LLM Serving, RAG & Fine-Tuning

High-throughput LLM inference serving, retrieval-augmented generation pipelines, prompt versioning, fine-tuning ops, cost analytics, and observability tracing across every chain step.

LLM serving & inference optimizationRAG pipelines & retrieval evaluationPrompt versioning & cost controlsRAGOps

LLMOps course

03Autonomous Systems

AgentOps — Agent Orchestration & Governance

Multi-agent workflows, tool calling, MCP integrations, drift detection across all layers, security guardrails, audit logging, and compliance frameworks for autonomous AI systems.

Multi-agent orchestration & tool callingDrift detection across all AI layersSecurity guardrails & audit logging

Audience/Role Selector

Who Is This AIOps Course For?

Built for engineers and technical leads already working with AI, ML, data, or platform systems who want production operations depth.

01 / SELECTED ROLE

AI Engineers & Architects

Designing and scaling production AI systems, model pipelines, and inference infrastructure across teams.

Prerequisites

Python proficiency, basic ML concepts, and experience with production systems or infrastructure.

CAREER OUTCOME

How This AIOps Course Supports Your Career Progression

Many engineers can build a model, RAG pipeline or AI agent. Far fewer can deploy it reliably, measure its performance, control its cost, diagnose failures and govern what happens after launch. This program is designed to expand your responsibility from individual AI features to complete production systems.

Reliability ownership

Move beyond accuracy scores to latency, throughput, drift, evaluation quality, uptime and production service levels.

Deployment ownership

Package, deploy, scale and safely update ML models, LLM services, RAG pipelines and agent workflows.

Operational ownership

Trace failures, respond to incidents, control infrastructure costs, implement security guardrails and document production decisions.

Relevant career paths

MLOps EngineerLLMOps EngineerAIOps EngineerAI Platform EngineerAI Infrastructure Architect

Career outcomes depend on previous experience, completed projects, interview performance and market opportunities.

Systems/Production Workbench

8 Production Systems You Will Build in This AIOps Course

Eight hands-on production-grade AI infrastructure projects, each mapped to a month of the program and integrated into your final capstone.

End-to-End Observability Pipeline

Trace every model call, agent interaction, and cost attribution with structured logging across the full stack.

LangSmithLangtraceOpenTelemetry

Multi-Model Drift Detection System

Monitor data drift, concept drift, and prompt drift with automated alerting and retraining triggers.

EvidentlyGreat ExpectationsGrafana

High-Performance LLM Serving Infrastructure

Serve LLMs with PagedAttention, continuous batching, and quantization tuned for latency and throughput.

vLLMTGIFastAPI

Agent Orchestration Platform

Build multi-agent workflows with tool calling, MCP integrations, and guardrails for autonomous systems.

LangGraphCrewAIMCP

Production RAG Pipeline

Deploy retrieval-augmented generation with vector databases, retrieval evaluation, and semantic monitoring.

LlamaIndexQdrantLangfuse

Cost Analytics Dashboard

Track token usage, GPU utilization, and budget controls across teams with threshold-based alerts.

GrafanaPrometheusLangfuse

CI/CD Pipeline for AI

Ship models with evaluation gates, A/B comparison, and rollback capabilities baked into release workflows.

GitHub ActionsDockerKubernetes

Governance Framework

Implement audit trails, compliance checks, and security policies for AI systems in regulated environments.

OpenTelemetryGuardrailsAudit Logs

08 / PRODUCTION SYSTEMS · ALL INTEGRATED INTO ONE CAPSTONE

PROOF OF WORK

Your Engineering Portfolio After Completing This AIOps Course

By the end of the program, your strongest outcome should not be a list of completed modules. It should be engineering work that a technical interviewer, hiring manager or architecture team can inspect, question and verify.

Working production systems

Eight version-controlled implementations covering ML pipelines, LLM serving, RAG, agent orchestration, observability, cost control, CI/CD and governance.

Measurable engineering results

Benchmark evidence covering latency, throughput, evaluation quality, drift, GPU utilisation and inference cost.

Operational documentation

Architecture diagrams, deployment configurations, monitoring dashboards, evaluation gates, operational runbooks and an incident postmortem.

Integrated AIOps capstone

One connected MLOps, LLMOps and AgentOps system reviewed for architecture, reliability, security, observability and operational decision-making.

₹80,000 · Six months · 15–20 hours per week · Live mentor-reviewed work

Your investment is directed towards reviewed production work you can demonstrate—not access to course content alone.

Small cohort · Every capstone reviewed

See the 6-Month Learning Roadmap

Curriculum Pillars/Capability Matrix

AIOps Course Overview — What You Will Learn

Six operational pillars define the scope of the program, from model pipelines and inference serving to observability, drift control, and governance.

PillarBuildDeployOperateOptimize

MLOps Foundations

Build reproducible ML pipelines with experiment tracking, model versioning, and CI/CD for model deployments.

Advanced

Strong

LLMOps & Serving

Deploy foundation models with high-throughput serving optimized for latency, throughput, and cost.

RAGOps

Advanced

Strong

Advanced

AgentOps & Orchestration

Build autonomous agents with multi-agent workflows, secure tool calling, and Model Context Protocol.

Strong

Advanced

Strong

Observability & Tracing

Instrument every model call with token-level tracing, cost analytics, and drift detection.

Strong

Advanced

Drift Detection

Monitor data drift, model drift, and prompt drift across the entire AI pipeline with automated detection.

Intermediate

Strong

Advanced

Security & Cost Control

Enforce guardrails, budget caps, and governance policies across all AI workloads in production.

Strong

Advanced

MLOps Foundations

Build reproducible ML pipelines with experiment tracking, model versioning, and CI/CD for model deployments.

BuildAdvanced

DeployAdvanced

OperateAdvanced

OptimizeStrong

LLMOps & Serving

Deploy foundation models with high-throughput serving optimized for latency, throughput, and cost.

RAGOps

BuildAdvanced

DeployAdvanced

OperateStrong

OptimizeAdvanced

AgentOps & Orchestration

Build autonomous agents with multi-agent workflows, secure tool calling, and Model Context Protocol.

BuildStrong

DeployStrong

OperateAdvanced

OptimizeStrong

Observability & Tracing

Instrument every model call with token-level tracing, cost analytics, and drift detection.

BuildStrong

DeployAdvanced

OperateAdvanced

OptimizeAdvanced

Drift Detection

Monitor data drift, model drift, and prompt drift across the entire AI pipeline with automated detection.

BuildIntermediate

DeployStrong

OperateAdvanced

OptimizeAdvanced

Security & Cost Control

Enforce guardrails, budget caps, and governance policies across all AI workloads in production.

BuildStrong

DeployStrong

OperateAdvanced

OptimizeAdvanced

Every pillar ends with a working system — traced, monitored, and deployed. Your capstone wires all six into one production-ready AIOps platform.

09 / Toolchain

AIOps Tools and Platforms You Will Use in This Course

Work across the production lifecycle — from development and packaging to training, serving, agents, tracing and infrastructure.

Agent + RAG Layer

LangGraphCrewAIAutoGenMCPQdrantLlamaIndex

04 / SERVE

Build and operate high-throughput model endpoints, manage GPU resources, optimize inference and expose production APIs.

vLLM · TGI · KServe · Ray Serve

04 / SERVE

vLLMTGIKServeRay Serve

Build and operate high-throughput model endpoints, manage GPU resources, optimize inference and expose production APIs.

08 / Learning Path

AIOps Course Roadmap — 6-Month Learning Path

Six months. One production stack that grows in complexity as you move from core MLOps foundations to LLM serving, AgentOps, observability and the final production AIOps capstone.

Stack Build · Month 01

DATATRAINDEPLOY

Core focus

MLOps Foundations & DevOps Essentials

Build

Dockerized ML API with health checks, versioned configuration and a CI pipeline.

Key topics

4 modules covering AIOps lifecycle…

Month 1

MLOps Foundations & DevOps Essentials

Build: Dockerized ML API with health checks, versioned configuration and a CI pipeline.

DATATRAINDEPLOY

Month 2

Data Pipelines & Experiment Tracking

Build: Reproducible ML pipeline with MLflow lineage, validation gates and data-drift monitoring.

PIPELINEMODEL REGISTRYEXPERIMENT TRACKING

Month 3

LLM Serving & Inference Optimization

Build: Deployed vLLM inference service with streaming, p95/p99 load tests, throughput results and GPU-utilization monitoring.

LLM RUNTIMEvLLM / TGISERVING

Month 4

AgentOps & Orchestration

Build: Production agent system with MCP integrations, an evaluated RAG pipeline and tool-level security controls.

RAGAGENT RUNTIMETOOLSRAGOps

Month 5

Observability, Tracing & Drift Detection

Build: Observability pipeline with model and prompt-drift alerts, token and GPU cost dashboards and an on-call runbook.

TRACINGOBSERVABILITYDRIFT

Month 6

Capstone — Production AIOps System

Build: Integrated AIOps capstone with evaluation-gated CI/CD, audit trails, governance controls, a security review and an incident postmortem.

MLLLMRAGAGENTSOBSERVABILITYGOVERNANCE

Integrated Production AIOps System

MLLLMRAGAGENTSOBSERVABILITYGOVERNANCE

10 / Curriculum

AIOps Course Syllabus

The six-month AIOps curriculum progresses from production MLOps foundations and data pipelines into distributed model infrastructure, high-performance LLM serving, RAGOps, AgentOps, observability, security, governance and enterprise AI operations. Across 23 structured sections, learners move from managing traditional ML lifecycles to operating connected ML, LLM and autonomous-agent systems in production.

23 Sections·6 Months·MLOps + LLMOps + AgentOps·8 Production Systems

FOUNDATIONMLOPSLLMOPSAGENTOPSOPERATIONS

FOUNDATION↓

MLOPS↓

LLMOPS↓

AGENTOPS↓

OPERATIONS

Phase 01: Production Foundations

Build the engineering fundamentals — Python, ML concepts, Git, Docker, Kubernetes and CI/CD — that every production AI system depends on.

MLOps Foundations (Lifecycle, Reproducibility, and Production Thinking)

Why MLOps matters: lifecycle, reproducibility and production thinking.

MLOPS

Python Essentials for MLOps

Scripting, data handling, environments and debugging for ML workflows.

MLOPS

Foundations of Machine Learning for MLOps

Core ML concepts, preprocessing, evaluation and model persistence for production.

MLOPS

Git Essentials for MLOps

Version control, branching and collaboration for ML teams.

MLOPS

Docker for MLOps

Containerizing training and serving environments for reproducibility.

MLOPS

Kubernetes for MLOps

Deploying inference APIs, scaling and running training jobs on Kubernetes.

MLOPS

CI/CD for MLOps

Automated testing, model deployment pipelines and rollback strategies.

MLOPS

Phase 02: MLOps Systems

Move from foundations into production data pipelines, experiment tracking and model registry workflows.

Data Pipelines for MLOps (Ingestion, Cleaning, Versioning, and Drift)

Production data pipelines with validation, versioning and drift monitoring.

MLOPS

Experiment Tracking & Model Registry (MLflow)

Tracking experiments, managing model versions and linking lineage.

MLOPS

Full Curriculum

Want the detailed lesson, lab and project breakdown? The complete syllabus includes all subtopics, lab sequences, tools, assignments and production project milestones across the six-month program.

PDF · Detailed 6-Month Curriculum

Download the Complete AIOps Syllabus

Phase 03: LLM Infrastructure

Move from packaged models to scalable GenAI runtimes, retrieval systems and production model operations.

High-Performance LLM Serving (vLLM, TGI, DeepSpeed)

Production inference, scaling and runtime optimization.

LLMOPS

Serving Infrastructure for GenAI (KServe, Ray Serve, LitServe, Helm)

KServe, Ray Serve and production model deployment patterns.

LLMOPS

Model Packaging, Serialization & Artifact Management (Shared)

Safe serialization, DVC, Hugging Face Hub and MLflow 3.0 registries.

SHARED

LLMOps Lifecycle – PromptOps & ModelOps (MLflow + LangSmith)

Prompt logging, registry management and model lineage for GenAI.

LLMOPS

RAGOps – Retrieval Infrastructure & Evaluation

RAG scheduling, vector DB architecture and retrieval quality monitoring.

LLMOPS

Phase 04: Agent + AI Operations

Operate autonomous agent systems, orchestration platforms and full-stack observability across ML and LLM workloads.

Model Context Protocol (MCP) for Secure Agent Integration

Secure tool execution, MCP architecture and agent framework integration.

LLMOPS

AgentOps – LangGraph, CrewAI, AutoGen

Graph-based agent orchestration, multi-agent management and observability.

AGENTOPS

Observability & Tracing (LangSmith, Langtrace, Langfuse + OpenTelemetry)

End-to-end tracing, dashboards and drift/hallucination detection.

LLMOPS

Cost Optimization & Token Analytics

Token-level logging, feedback loops and budget alerting.

LLMOPS

Phase 05: Reliability + Enterprise

Secure, govern and scale AI systems for enterprise and multi-cloud production environments.

Security & Abuse Prevention in AI Infrastructure

Auth, prompt injection defense, rate limiting and secrets management.

LLMOPS

Governance, Compliance & Red Teaming

Regulatory compliance, prompt governance and adversarial testing.

LLMOPS

Multi-Cloud & Hybrid AI Deployment

Cross-provider LLM APIs, failover and hybrid cloud + local model strategies.

LLMOPS

AIOps Extra – Observability for Infra + Incident Operations

Infra metrics, centralized logging, tracing and incident management.

EXTRA

AIOps Extra – SIEM/SOC + Enterprise Tooling Integration

Security monitoring, enterprise platforms and incident workflows.

EXTRA

23 Sections·6 Months·8 Production Systems

MLOps → LLMOps → AgentOps → Production Operations

Download Full AIOps Syllabus Request a Course Walkthrough →

Why This Program/Evidence Ledger

Why Choose This AIOps Course?

Built for engineers who want production depth, hands-on mentorship, and deployment discipline — not a survey of tools.

0108 CONNECTED SYSTEMS

Eight Connected Production Systems

Every module ships a working system — from observability pipelines to agent orchestration — and your capstone wires them into one production AIOps platform.

02ENGINEERING REVIEWS

Mentorship and Engineering Reviews

PR-style code reviews from practising AIOps engineers, with feedback on pipeline design, serving configs, and operational readiness across every project.

03OPERATIONS DRILLS

Production Operations Drills

Simulated incidents — latency spikes, drift regressions, budget breaches — where you triage, respond, and write a postmortem the way on-call engineers do.

04CAPSTONE ASSESSMENT

Capstone Assessment and Portfolio Review

Your integrated capstone is reviewed for CI/CD quality, observability coverage, and governance before it becomes a portfolio-ready artifact you can showcase.

05CAREER SUPPORT

Career and Placement Support

Resume positioning around your deployed systems, mock interviews with engineers, and placement assistance throughout your job search after completion.

11 / Credential/Verified Record

Your Certificate After Completing the AIOps Course

Earn a professional credential by completing the required production systems, final AIOps capstone and technical review.

The credential records the production-AI capabilities demonstrated across MLOps, LLMOps and AgentOps.

Credential

SCAIProfessional Credential

AIOPS

PROFESSIONAL

Production AI Operations

MLOpsLLMOpsAgentOps

LevelProfessional

IssuerSchool of Core AI

Credential IDAIOPS-26-XXXX

Verified on Completion

Capability Record

01ML Production Systems

Training lifecycle · Model registry · Deployment · Drift

02LLM Operations

Inference · Serving · RAG · Evaluation

03Agent Operations

Orchestration · Tools · Tracing · Guardrails

04Production Operations

Observability · Cost · Security · Governance

Earning Criteria

01Required production systems

8 system builds

02Capstone

Integrated production AIOps architecture

03Engineering review

Architecture + implementation review

04Final assessment

Technical completion criteria

Verification

Credential ID included on completion. Built systems are reviewed as part of the completion process.

Built systems are reviewed as part of the completion process.

See the 8 Production Systems

12 / Choose Your Scope

MLOps vs LLMOps vs AIOps — Which Course Is Right for You?

Choose based on the production systems you want to own — traditional ML, language-model infrastructure or the complete production AI stack.

MLOps

ML SYSTEMS

LLMOps

ML + LANGUAGE MODEL OPERATIONS

AIOps

FULL PRODUCTION AI STACK

Current

CapabilityMLOpsLLMOpsAIOps

Traditional ML●●●

Experiment tracking●●●

Model registry●●●

LLM serving—●●

RAG systems—●●

Prompt / evaluation—●●

Agent orchestration——●

Observability●●●

Drift management●●●

Cost optimization—●●

Governance——●

Full stack integration——●

MLOps

ML SYSTEMS

ML lifecycleExperiment trackingRegistryDeploymentMonitoring

LLMOps

ML + LANGUAGE MODEL OPERATIONS

LLM servingRAGPrompt lifecycleEvaluationTracing

AIOpsCurrent Program

FULL PRODUCTION AI STACK

MLLLMsRAGAgentsObservabilityDriftGovernanceCost

If your target is

ML PLATFORM ENGINEERING→ MLOps

GENAI / LLM INFRASTRUCTURE→ LLMOps

FULL PRODUCTION AI SYSTEMS→ AIOps

Related courses and comparisons

Generative AI Specialization

Advanced LLM architectures, multimodal models, RAG design patterns, and agentic deployment.

MLOps Course vs AIOps Course

Understand when ML lifecycle depth is enough and when broader AI operations scope is the better move.

MLOps vs LLMOps vs AIOps

Compare operations tracks for classical ML, LLM systems, and broader AI platform work.

MLOps Engineer vs ML Engineer

Understand whether you want to own ML models themselves or the production systems around them.

13 / Program Fee

AIOps Course Fees & Payment Plans

The ₹80,000 program fee covers the complete six-month live AIOps program, including mentor-led sessions, eight production-system builds, engineering reviews, capstone assessment and certification.

Program Fee

₹80,000

EMI Available·Payment plans available

Duration

6 Months

Format

Live Online

Systems

8 Production Builds

Commitment

15–20 hrs / Week

What the Program Fee Covers

LIVE COHORT

Instructor-led live sessions throughout the program.

PRODUCTION SYSTEMS

Eight connected engineering builds.

ENGINEERING REVIEW

Architecture and implementation feedback.

OPERATIONS DRILLS

Failure, latency, drift and reliability scenarios.

CAPSTONE ASSESSMENT

Integrated final AIOps system.

CERTIFICATION

Professional credential after completion requirements.

CAREER SUPPORT

Portfolio, interview and placement support after completion.

Evaluate First

Want to evaluate the program first?

Walk through the curriculum, project expectations, cohort format and payment options before making an enrolment decision.

Request a Course Walkthrough Discuss EMI Options →

Additional Project Costs

Cloud, GPU and third-party subscriptions required for individual project work are not included in the program fee.

Enrolment/Process

How to Enrol in the AIOps Course

No entrance exam. No lengthy admissions process. Four simple steps to start your AIOps career.

Request a Walkthrough

Get a short overview of the curriculum, tooling, and how it maps to your current stack and role.

Request a Course Walkthrough

Speak with an Advisor

Align the program with your role — AI engineer, MLOps, SRE, or data infrastructure lead — before enrolling.

Speak with an Advisor

Enrol & Get Access

Complete payment (one-time or EMI). Get immediate access to pre-work and cohort onboarding materials.

Start Building

Join your cohort, set up your dev environment, and start deploying production systems from Month 1.

Request a Walkthrough

Get a short overview of the curriculum, tooling, and how it maps to your current stack and role.

Request a Course Walkthrough

Speak with an Advisor

Align the program with your role — AI engineer, MLOps, SRE, or data infrastructure lead — before enrolling.

Speak with an Advisor

Enrol & Get Access

Complete payment (one-time or EMI). Get immediate access to pre-work and cohort onboarding materials.

Start Building

Join your cohort, set up your dev environment, and start deploying production systems from Month 1.

Immediate access to pre-work is available after enrolment. Request a Course Walkthrough is the first step.

Career Outcomes

Engineers Who Built Production AI Systems After This Course

Twelve engineers share the specific gap this AIOps course filled — and the role they moved into after shipping production AI.

DevOps Engineer → AI Infrastructure Engineer

I had Kubernetes experience but zero idea how ML workloads were different from regular services. The way this course handled containerisation for model serving, GPU resource scheduling, and production-grade CI/CD specifically for AI — that was the gap I needed filled. I stopped being the guy who "knows DevOps" and became the guy who owns the AI infra layer. Got promoted within the same org three months after finishing.

Rahul Sharma

AI Infrastructure Engineer, Wipro

Backend Engineer → MLOps Engineer

I was writing APIs for ML teams but had no idea what happened on the other side — how models got trained, versioned, or retrained safely. Going through the data pipeline and experiment tracking modules properly changed how I think about the entire system. Now I own the MLflow setup and retraining pipelines for two product teams. It is a completely different job and a better one.

Amit Verma

MLOps Engineer, Persistent Systems

Data Analyst → Data + MLOps Engineer

Data pipelines I knew. But versioning datasets for ML, handling drift in training data, and setting up a proper model registry — that was new territory. The MLOps systems phase connected everything I already knew about data to what production ML actually needs. I was able to move into a platform engineering role because I could show I had built these workflows, not just read about them.

Priya Gupta

ML Platform Engineer, PhonePe

Questions

AIOps Course — Frequently Asked Questions

Direct answers about the AIOps course, fees, certification, and what you will build.

AIOps Course in India

What Is AIOps? MLOps, LLMOps & AgentOps Explained

The AIOps Course Stack — From ML Pipelines to Autonomous Agents

MLOps — Model Lifecycle & Pipelines

LLMOps — LLM Serving, RAG & Fine-Tuning

AgentOps — Agent Orchestration & Governance

Who Is This AIOps Course For?

AI Engineers & Architects

MLOps & Data Engineers

ML Practitioners Moving into Production AI

DevOps / SRE / Platform Engineers

Engineering & Technical Leads

How This AIOps Course Supports Your Career Progression

8 Production Systems You Will Build in This AIOps Course

End-to-End Observability Pipeline

Multi-Model Drift Detection System

High-Performance LLM Serving Infrastructure

Agent Orchestration Platform

Production RAG Pipeline

Cost Analytics Dashboard

CI/CD Pipeline for AI

Governance Framework

Your Engineering Portfolio After Completing This AIOps Course

AIOps Course Overview — What You Will Learn

MLOps Foundations

LLMOps & Serving

AgentOps & Orchestration

Observability & Tracing

Drift Detection

Security & Cost Control

AIOps Tools and Platforms You Will Use in This Course

AIOps Course Roadmap — 6-Month Learning Path

MLOps Foundations & DevOps Essentials

Data Pipelines & Experiment Tracking

LLM Serving & Inference Optimization

AgentOps & Orchestration

Observability, Tracing & Drift Detection

Capstone — Production AIOps System

AIOps Course Syllabus

Phase 01: Production Foundations

MLOps Foundations (Lifecycle, Reproducibility, and Production Thinking)

Python Essentials for MLOps

Foundations of Machine Learning for MLOps

Git Essentials for MLOps

Docker for MLOps

Kubernetes for MLOps

CI/CD for MLOps

Phase 02: MLOps Systems

Data Pipelines for MLOps (Ingestion, Cleaning, Versioning, and Drift)

Experiment Tracking & Model Registry (MLflow)

Phase 03: LLM Infrastructure

High-Performance LLM Serving (vLLM, TGI, DeepSpeed)

Serving Infrastructure for GenAI (KServe, Ray Serve, LitServe, Helm)

Model Packaging, Serialization & Artifact Management (Shared)

LLMOps Lifecycle – PromptOps & ModelOps (MLflow + LangSmith)

RAGOps – Retrieval Infrastructure & Evaluation

Phase 04: Agent + AI Operations

Model Context Protocol (MCP) for Secure Agent Integration

AgentOps – LangGraph, CrewAI, AutoGen

Observability & Tracing (LangSmith, Langtrace, Langfuse + OpenTelemetry)

Cost Optimization & Token Analytics

Phase 05: Reliability + Enterprise

Security & Abuse Prevention in AI Infrastructure

Governance, Compliance & Red Teaming

Multi-Cloud & Hybrid AI Deployment

AIOps Extra – Observability for Infra + Incident Operations

AIOps Extra – SIEM/SOC + Enterprise Tooling Integration

Why Choose This AIOps Course?

Eight Connected Production Systems

Mentorship and Engineering Reviews

Production Operations Drills

Capstone Assessment and Portfolio Review

Career and Placement Support

Your Certificate After Completing the AIOps Course

MLOps vs LLMOps vs AIOps — Which Course Is Right for You?

AIOps Course Fees & Payment Plans

Want to evaluate the program first?

How to Enrol in the AIOps Course

Request a Walkthrough

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