AIOps Certification Course in India

4 Months Featured Architect level

Every layer of production AI — ML pipelines, distributed training, LLM serving, RAG systems, and autonomous agents — built by you, from scratch.

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Program fee

₹80,000

One-time payment

What Is AIOps? MLOps, LLMOps & AgentOps Explained

AIOps (AI Operations) is the discipline of engineering reliable, observable, and scalable AI systems in production. It encompasses MLOps for traditional machine learning models, LLMOps for large language model serving and optimization, and AgentOps for autonomous AI agent systems — integrating monitoring, drift detection, inference optimization, and governance across the entire AI lifecycle. Modern AIOps bridges the gap between AI research and production infrastructure, ensuring AI systems perform reliably under real-world conditions with proper observability, cost control, and operational excellence. This AIOps course trains you to master all three pillars.

The AIOps Stack — From ML Pipelines to Autonomous Agents

Most courses teach one layer. AIOps teaches all three — the classical ML foundation, the LLM and RAG layer on top of it, and the agent systems that orchestrate everything.

Traditional ML — 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 & versioning Experiment tracking & model registry CI/CD & automated retraining

Language Models — Serving, RAG & Fine-Tuning

High-throughput LLM inference serving, retrieval-augmented generation (RAG) pipelines, prompt versioning, fine-tuning ops, cost analytics, and observability tracing across every chain step. LLM serving & inference optimization RAG pipelines & retrieval evaluation Prompt versioning & cost controls

Autonomous Agents — 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 calling Drift detection across all AI layers Security guardrails & audit logging

Who Is This AIOps Course For?

Built for engineers and technical leads already working with AI, ML, data, or platform systems:

AI Engineers & Architects

designing and scaling production AI systems, model pipelines, and inference infrastructure

MLOps & Data Engineers

building reliable ML pipelines, experiment tracking, and automated retraining workflows

ML Practitioners Moving into Production AI

taking models beyond notebooks into deployment, monitoring, drift detection, and operational ownership

DevOps / SRE / Platform Engineers

managing AI infrastructure, GPU clusters, model serving, and observability pipelines

Engineering & Technical Leads

architecting AI platforms, establishing MLOps/LLMOps practices, and leading data infrastructure teams

8 Production Systems You Will Build in This AIOps Course

Hands-on production-grade AI infrastructure projects you will build and deploy:

End-to-end observability pipeline

with LangSmith/Langtrace for tracing every model call, agent interaction, and cost attribution

Multi-model drift detection system

monitoring data drift, concept drift, and prompt drift with automated alerting

High-performance LLM serving infrastructure

using vLLM v0.4+ with PagedAttention, quantization, and auto-scaling for real production workloads

Agent orchestration platform

with LangGraph 0.1+ for multi-agent workflows, Model Context Protocol (MCP), and guardrails

Production RAG pipeline

with vector databases, retrieval evaluation, and semantic monitoring

Cost analytics dashboard

tracking token usage, GPU utilization, and budget controls across teams

CI/CD pipeline for AI

with model evaluation gates, A/B testing, and rollback capabilities

Governance framework

implementing audit trails, compliance checks, and security policies for AI systems

Course Curriculum

Traditional ML operations
Module 1: Pipelines
  • Data pipelines and versioning
  • Experiment tracking
  • Model registry
Module 2: Retraining
  • CI/CD and automated retraining
  • Evaluation Pipelines
Deep learning in production
Module 3: GPU serving
  • LLM serving and inference optimization
  • Quantization
  • Scaling Strategies
Enterprise AI ops
Module 4: LLMOps and AgentOps
  • Multi-agent orchestration
  • Drift across AI layers
  • Security guardrails
Module 5: Governance
  • Enterprise Governance
  • Audit logging
  • Automation Pipelines

Frequently asked questions

What Is AIOps? MLOps, LLMOps & AgentOps Explained

AIOps (AI Operations) is the discipline of engineering reliable, observable, and scalable AI systems in production. It encompasses MLOps for traditional machine learning models, LLMOps for large language model serving and optimization, and AgentOps for autonomous AI agent systems — integrating monitoring, drift detection, inference optimization, and governance across the entire AI lifecycle. Modern AIOps bridges the gap between AI research and production infrastructure, ensuring AI systems perform reliably under real-world conditions with proper observability, cost control, and operational excellence. This AIOps course trains you to master all three pillars.

Who Is This AIOps Course For?

Built for engineers and technical leads already working with AI, ML, data, or platform systems:

Why Choose This AIOps Certification Program?

Built for AI engineers and technical leads who want production depth, hands-on mentorship, and deployment discipline — not lightweight survey content. Full-Stack AIOps Coverage Master MLOps, LLMOps, and AgentOps in one unified track — from ML pipelines to LLM serving to autonomous agent orchestration. Multi-Layer Drift Detection Detect and mitigate data drift, model drift, and prompt drift using Evidently, custom pipelines, and automated alerting workflows. Production Observability Stack End-to-end tracing with LangSmith, Langtrace, and OpenTelemetry — token-level cost tracking, latency profiling, and error diagnostics. LLMOps & Inference Optimization Deploy models with vLLM, TGI, and LangServe — continuous batching, quantization tradeoffs, and p95/p99 latency optimization. AgentOps & MCP

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

MLOps Course: Master end-to-end ML workflows — from versioning and CI/CD to scalable model serving with Docker, Kubernetes, and MLflow. LLMOps Course: Specialize in LLM deployment — covering quantization, vLLM, LangServe, LangSmith, distributed inference, and cost optimization. AIOps Course: The all-in-one track — covering MLOps, LLMOps, and AgentOps. Dive deep into drift detection, PromptOps, RAG pipelines, and secure agent deployment. Explore MLOps Explore LLMOps

How to Enrol in the AIOps Course

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

Ready to start?

Talk to an advisor about this program — 15 minutes, no sales pitch.

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