AIOps Course
Master MLOps, LLMOps & AgentOps in One Program
Learn to build, deploy, monitor and operate AI applications end to end — from ML pipelines and LLM serving to RAG, autonomous agents and production observability.
Six months of live, hands-on training. Build eight connected production AI systems with guided engineering labs, mentor reviews and an integrated capstone.
Live Online Training
Hands-on Production Builds
Hours per Week
EMI Available
Mentor-Led · Engineering Reviews · Certificate · Career Support
+91 96914 40998One Program. The End-to-End AI Application Lifecycle.
Kubernetes, observability, reliability and deployment connect these three areas across the entire program — so you learn how the layers work together in real production environments.
MLOps
Build reproducible ML pipelines, manage experiments, deploy models and monitor them in production.
Explore MLOps Course →LLMOps
Deploy and optimize LLM services, evaluate RAG applications, manage inference costs and monitor model behavior.
Explore LLMOps Course →AgentOps
Operate AI agents and multi-agent systems with orchestration, tracing, tool security, evaluation and governance.
Not traditional IT AIOps. This program focuses on production AI operations — deploying, monitoring and governing AI applications. Traditional IT AIOps uses AI for infrastructure monitoring, anomaly detection and incident response, which is a different discipline.
What Is AIOps? MLOps, LLMOps & AgentOps Explained
AIOps (AI Operations) in this program is the discipline of engineering reliable, observable, and scalable AI systems in production. While traditional AIOps focuses on applying AI to IT operations and event correlation, this curriculum unifies MLOps for the model lifecycle, LLMOps for LLM serving and prompt management, and AgentOps for autonomous-agent orchestration—covering the entire production AI application lifecycle.
For modern AI teams, AIOps connects research with production infrastructure. The core lifecycle progresses from Data → Training → Evaluation → Deployment → Monitoring → Optimization → Governance, ensuring 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.
Operate the complete machine-learning lifecycle from data and training to deployment, monitoring and retraining.
Operate large language models with prompt management, evaluation, scalable serving, tracing and inference optimization.
Operate agent workflows across orchestration, tools, memory, runtime tracing, evaluation and guardrails.
AIOPS / Shared Production Layer
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.
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.
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.
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.
Who Should Join This AIOps Course?
If you are already working with AI, ML, data, or platform systems and want to go deeper into production operations — this course is built for you.
AI Engineers & Architects
You already design AI systems and guide teams on model choices. This course fills the production gap — you will learn to deploy, monitor, and operate those systems reliably at scale.
Prerequisites
You should be comfortable with Python, have a basic understanding of ML concepts, and some experience working with production systems or cloud infrastructure.
MLOps & Data Engineers
You build pipelines and track experiments today. Here you go beyond traditional ML workflows into LLM serving, agent orchestration, and the observability layer that ties everything together.
ML Practitioners Moving into Production AI
You can train models in notebooks but have not shipped them to production. This course takes you from local experimentation to deployment, monitoring, drift detection, and full operational ownership.
DevOps / SRE / Platform Engineers
You manage infrastructure and keep services running. Now you will add AI-specific tooling to your stack — model serving, LLM tracing, drift alerting, and cost controls built for AI workloads.
Engineering & Technical Leads
You lead teams and make architecture decisions. This course gives you the depth to set up MLOps and LLMOps practices, choose the right tools, and own AI platform strategy with confidence.
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.
Multi-Model Drift Detection System
Monitor data drift, concept drift, and prompt drift with automated alerting and retraining triggers.
High-Performance LLM Serving Infrastructure
Serve LLMs with PagedAttention, continuous batching, and quantization tuned for latency and throughput.
Agent Orchestration Platform
Build multi-agent workflows with tool calling, MCP integrations, and guardrails for autonomous systems.
Production RAG Pipeline
Deploy retrieval-augmented generation with vector databases, retrieval evaluation, and semantic monitoring.
Cost Analytics Dashboard
Track token usage, GPU utilization, and budget controls across teams with threshold-based alerts.
CI/CD Pipeline for AI
Ship models with evaluation gates, A/B comparison, and rollback capabilities baked into release workflows.
Governance Framework
Implement audit trails, compliance checks, and security policies for AI systems in regulated environments.
08 / PRODUCTION SYSTEMS · ALL INTEGRATED INTO ONE CAPSTONE
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 · 4–5 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 RoadmapHow 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
Career outcomes depend on previous experience, completed projects, interview performance and market opportunities.
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.
MLOps Foundations
Build reproducible ML pipelines with experiment tracking, model versioning, and CI/CD for model deployments.
LLMOps & Serving
Deploy foundation models with high-throughput serving optimized for latency, throughput, and cost.
RAGOpsAgentOps & Orchestration
Build autonomous agents with multi-agent workflows, secure tool calling, and Model Context Protocol.
Observability & Tracing
Instrument every model call with token-level tracing, cost analytics, and drift detection.
Drift Detection
Monitor data drift, model drift, and prompt drift across the entire AI pipeline with automated detection.
Security & Cost Control
Enforce guardrails, budget caps, and governance policies across all AI workloads in production.
MLOps Foundations
Build reproducible ML pipelines with experiment tracking, model versioning, and CI/CD for model deployments.
LLMOps & Serving
Deploy foundation models with high-throughput serving optimized for latency, throughput, and cost.
RAGOpsAgentOps & Orchestration
Build autonomous agents with multi-agent workflows, secure tool calling, and Model Context Protocol.
Observability & Tracing
Instrument every model call with token-level tracing, cost analytics, and drift detection.
Drift Detection
Monitor data drift, model drift, and prompt drift across the entire AI pipeline with automated detection.
Security & Cost Control
Enforce guardrails, budget caps, and governance policies across all AI workloads in production.
Every pillar ends with a working system — traced, monitored, and deployed. Your capstone wires all six into one production-ready AIOps platform.
How Our Live AIOps Training Works
This is not a pre-recorded bootcamp. It is a professional engineering programme where you move from attending sessions to owning production systems.
Live Instructor-Led Sessions
Deep-dive into core AIOps concepts with real-time interaction, live debugging, and architecture walkthroughs.
Guided Implementation Labs
Apply theory immediately through hands-on labs. Build the components of the 8 production systems step-by-step.
Code and Architecture Reviews
Submit your implementation for PR-style reviews. Get critical feedback on reliability, latency, and production readiness.
Production-Operations Exercises
Simulate real-world failures — drift spikes, latency breaches, and budget leaks — and learn to triage and respond.
Integrated Capstone Assessment
Wire all systems into one production AIOps platform. Final review of your portfolio evidence and technical completion.
Sessions vs. Implementation
While live sessions provide the conceptual framework and architectural guidance, the real engineering happens in the labs. You are expected to independently complete the production builds, utilizing mentor reviews to refine your implementation before final submission.
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.
Build and operate high-throughput model endpoints, manage GPU resources, optimize inference and expose production APIs.
vLLM · TGI · KServe · Ray Serve
Build and operate high-throughput model endpoints, manage GPU resources, optimize inference and expose production APIs.
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.
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…
MLOps Foundations & DevOps Essentials
Build: Dockerized ML API with health checks, versioned configuration and a CI pipeline.
Data Pipelines & Experiment Tracking
Build: Reproducible ML pipeline with MLflow lineage, validation gates and data-drift monitoring.
LLM Serving & Inference Optimization
Build: Deployed vLLM inference service with streaming, p95/p99 load tests, throughput results and GPU-utilization monitoring.
AgentOps & Orchestration
Build: Production agent system with MCP integrations, an evaluated RAG pipeline and tool-level security controls.
Observability, Tracing & Drift Detection
Build: Observability pipeline with model and prompt-drift alerts, token and GPU cost dashboards and an on-call runbook.
Capstone — Production AIOps System
Build: Integrated AIOps capstone with evaluation-gated CI/CD, audit trails, governance controls, a security review and an incident postmortem.
Integrated Production AIOps System
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.
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.
Python Essentials for MLOps
Scripting, data handling, environments and debugging for ML workflows.
Foundations of Machine Learning for MLOps
Core ML concepts, preprocessing, evaluation and model persistence for production.
Git Essentials for MLOps
Version control, branching and collaboration for ML teams.
Docker for MLOps
Containerizing training and serving environments for reproducibility.
Kubernetes for MLOps
Deploying inference APIs, scaling and running training jobs on Kubernetes.
CI/CD for MLOps
Automated testing, model deployment pipelines and rollback strategies.
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.
Experiment Tracking & Model Registry (MLflow)
Tracking experiments, managing model versions and linking lineage.
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
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.
Serving Infrastructure for GenAI (KServe, Ray Serve, LitServe, Helm)
KServe, Ray Serve and production model deployment patterns.
Model Packaging, Serialization & Artifact Management (Shared)
Safe serialization, DVC, Hugging Face Hub and MLflow 3.0 registries.
LLMOps Lifecycle – PromptOps & ModelOps (MLflow + LangSmith)
Prompt logging, registry management and model lineage for GenAI.
RAGOps – Retrieval Infrastructure & Evaluation
RAG scheduling, vector DB architecture and retrieval quality monitoring.
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.
AgentOps – LangGraph, CrewAI, AutoGen
Graph-based agent orchestration, multi-agent management and observability.
Observability & Tracing (LangSmith, Langtrace, Langfuse + OpenTelemetry)
End-to-end tracing, dashboards and drift/hallucination detection.
Cost Optimization & Token Analytics
Token-level logging, feedback loops and budget alerting.
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.
Governance, Compliance & Red Teaming
Regulatory compliance, prompt governance and adversarial testing.
Multi-Cloud & Hybrid AI Deployment
Cross-provider LLM APIs, failover and hybrid cloud + local model strategies.
AIOps Extra – Observability for Infra + Incident Operations
Infra metrics, centralized logging, tracing and incident management.
AIOps Extra – SIEM/SOC + Enterprise Tooling Integration
Security monitoring, enterprise platforms and incident workflows.
MLOps → LLMOps → AgentOps → Production Operations
Why Choose This AIOps Course?
Built for engineers who want production depth, hands-on mentorship, and deployment discipline — not a survey of tools.
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.
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.
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.
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.
Career and Placement Support
Resume positioning around your deployed systems, mock interviews with engineers, and placement assistance throughout your job search after completion.
AIOps Certification — What You Earn and How It Works
When you complete all eight production systems, the integrated capstone and the engineering review, you receive the AIOps Professional Certificate from School of Core AI.
It records the specific production-AI capabilities you demonstrated across MLOps, LLMOps and AgentOps — not just that you attended sessions, but that your systems passed review.
Production AI Operations
Training lifecycle · Model registry · Deployment · Drift
Inference · Serving · RAG · Evaluation
Orchestration · Tools · Tracing · Guardrails
Observability · Cost · Security · Governance
8 system builds
Integrated production AIOps architecture
Architecture + implementation review
Technical completion criteria
Credential ID included on completion.
MLOps vs LLMOps vs AIOps — Which Course Is Right for You?
Choose based on the production systems you want to own — starting from traditional ML, expanding to language-model infrastructure, or mastering the complete production AI stack.
ML SYSTEMS
ML + LANGUAGE MODEL OPERATIONS
FULL PRODUCTION AI STACK
If your target is
Related courses and comparisons
MLOps Certification Course
The foundation for AIOps. Focus on ML lifecycle, experiment tracking, and reproducible pipelines for traditional ML.
LLMOps Certification Course
The next step in the chain. Specialize in LLM serving, RAG pipelines, and prompt engineering at scale.
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.
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
Duration
6 Months
Format
Live Online
Systems
8 Production Builds
Commitment
4–5 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.
Additional Project Costs
Cloud, GPU and third-party subscriptions required for individual project work are not included in the program fee.
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 WalkthroughSpeak with an Advisor
Align the program with your role — AI engineer, MLOps, SRE, or data infrastructure lead — before enrolling.
Speak with an AdvisorEnrol & 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 WalkthroughSpeak with an Advisor
Align the program with your role — AI engineer, MLOps, SRE, or data infrastructure lead — before enrolling.
Speak with an AdvisorEnrol & 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. Get Course Details is the first step.
Engineers Who Moved Into Production AI After This Course
Working professionals who upskilled through SCAI — career switchers, senior engineers, and managers who chose to grow in AI operations.
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
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
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
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
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
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 SpecializationQuestions
AIOps Course — Frequently Asked Questions
Direct answers about the AIOps course, fees, certification, and what you will build.