Handy course syllabus

MLOps, AIOps, LLMOps, AI Agents & FDE Syllabus

Everything students ask for before joining — full module list, labs, projects, who it is for, and how to contact for batch details.

Masterclass syllabus AI Automation syllabus WhatsApp for batch details

Duration

4-5 months

Live fee

₹40,000 (2 installments)

Recordings

₹30,000 lifetime

Focus

MLOps · AIOps · LLMOps · Agents · FDE

Flagship

Masterclass curriculum (6 modules)

Job-ready path from fundamentals to production: DevOps foundations, MLOps, LLMOps, AIOps, AI Agents, FDE skills, and 4 portfolio capstones.

Module 1

DevOps Fundamentals for MLOps

Month 1

Linux, Python, Git, Docker, Kubernetes, IaC, CI/CD, cloud and monitoring foundations for ML teams.

Module 2

MLOps: Machine Learning Operations

Month 2

Automated ML pipelines from experimentation to production with tracking, versioning, deployment and monitoring.

Module 3

LLMOps: Large Language Model Operations

Month 3

Deploy, manage and optimize LLMs in production with RAG, fine-tuning, guardrails and observability.

Module 4

AIOps: AI for IT Operations

Month 4

AI-powered monitoring, prediction and remediation for modern infrastructure.

Module 5

AI Agents and Autonomous Systems

Month 4-5

Build agents that reason, plan, use tools and deploy safely in enterprise environments.

Module 6

Real-World Capstone Projects

Month 5

Four portfolio-ready projects covering MLOps, LLMOps, AIOps and enterprise AI agents.

Linux and Shell Scripting

·Linux commands for ML workflows
·Bash scripting and automation
·Process and network basics

Python for ML/AI-Ops

·Core Python, OOP and error handling
·Data structures, APIs and concurrency
·ML and ops libraries, testing and logging

Git and Version Control

·Git workflows and branching for ML
·Pull requests, collaboration and hooks

Docker ContainerizationLab

·Dockerfile best practices for ML apps
·Networking, volumes and Docker Compose

Kubernetes for MLLab

·Pods, Deployments, Services
·ConfigMaps, Secrets and persistent volumes

Infrastructure as CodeLab

·Terraform, Ansible and cloud templates

CI/CD Pipelines

·Jenkins and GitHub Actions for ML
·Automated test and deploy pipelines

Cloud Computing

·AWS, Azure, GCP comparison
·SageMaker, Vertex AI, Azure ML
·Cost and architecture patterns

Monitoring and ObservabilityLab

·Prometheus, Grafana and ELK/EFK
·Distributed tracing

Program includes

Live classes: Weekly live sessions with Q&A
Training: 150+ hours hands-on
Labs: 50+ practical exercises
Projects: 4 capstone portfolio builds
Mentorship: 1-on-1 career guidance
Career: Resume, LinkedIn and interview prep
Support: Job assistance and placement help

Who this is for

·Software engineers moving to MLOps
·DevOps engineers expanding into ML
·Data scientists going to production
·ML engineers deepening operations skills
·IT professionals exploring AI automation

Prerequisites

·Basic programming knowledge (freshers welcome)
·Software development concepts
·Command line familiarity
·Motivation to build production systems

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New

AI Automation syllabus (2 months)

Build company AI agents with Cursor, MCP, RAG, Bedrock, and business metrics. Fee: ₹20,000 with lifetime recordings.

Module 1

Ship Mode, Vibe Coding, GSD & Dev Velocity

Week 1 2

Vibe coding with Cursor, GSD execution with Codex, rapid UI with Lovable, the velocity layer every agent builder needs.

Outcome: Shipped feature + ticket-to-PR automation

·Vibe coding with Cursor: Agent mode and codebase-aware prompts
·Vibe coding with Cursor: Fix bugs from Jira ticket text
·Vibe coding with Cursor: Multi-file refactors and test generation
·GSD with Codex: Task in → PR out workflows
·GSD with Codex: CI failure investigation
·GSD with Codex: Review checkpoints, trust but verify
·Rapid UI: Lovable and Bolt for internal dashboards
·Rapid UI: When prototype is enough vs custom code

Module 2

LLM APIs, Local Models & Document Automation

Week 2 3

ChatGPT, Claude, Gemini, DeepSeek APIs plus Ollama and LM Studio for private automations. Structured outputs for business data.

Outcome: Document triage automation with JSON output

·LLM APIs: Multi-model APIs from one codebase
·LLM APIs: Structured JSON with Pydantic
·LLM APIs: Tool calling patterns
·Local models: Ollama and LM Studio, on-prem AI
·Local models: Zero API bills for sensitive data
·Document automation: Extract from invoices and PDFs
·Document automation: Classify and route items
·Document automation: Lab: triage project

Module 3

RAG Agent + Organization Runbooks

Week 3 4

Build RAG agents grounded in your company's docs. Learn to ingest, update, and version runbooks as processes change, the backbone of every enterprise agent.

Outcome: RAG agent with org-specific runbooks and citations

·RAG architecture: Chunk, embed, retrieve, generate
·RAG architecture: Source citations interviewers expect
·RAG architecture: Eval suite: pass/fail before ship
·Organization runbooks: Ingest wikis, policies, and SOPs
·Organization runbooks: Auto-update when processes change
·Organization runbooks: Incident and onboarding playbook patterns
·Hands-on: Lab: runbook Q&A agent for your domain

Module 4

MCP Agent, Jira, Slack & GitHub

Week 4 5

MCP connects AI to live systems. Wire agents to Jira tickets, Slack channels, GitHub repos, and databases, the integration layer companies pay for.

Outcome: Agent connected to Jira + Slack + data source

·MCP from zero: Build MCP server and client
·MCP from zero: Connect LLM to live data
·MCP from zero: Credential handling
·Enterprise connectors: Jira ticket automation
·Enterprise connectors: Slack summaries and approvals
·Enterprise connectors: GitHub issues and PRs
·Hands-on: Lab: incident → Jira + Slack flow

Module 5

Business Agents + Metrics Dashboard

Week 5 6

Build 3 business agents from the gallery. Every build includes business-level metrics, time saved, cost reduced, deflection rate, in a dashboard your leadership understands.

Outcome: 3 agents with metrics one-pagers

·Agent frameworks: LangChain, CrewAI, LangGraph
·Agent frameworks: n8n visual pipelines
·Agent frameworks: Multi-agent handoffs
·HR & people agents: Onboarding per organization
·HR & people agents: HR handbook bot
·HR & people agents: Offboarding templates
·Metrics that matter: Time-saved and cost-reduced calculators
·Metrics that matter: ROI one-pager template
·Metrics that matter: Lab: 3 agents + metrics dashboard

Module 6

Deploy, Innovate & Get Hired

Week 7 8

Deploy on Bedrock, Azure AI Foundry, and Vertex AI. Capstone connected flow. New cohort ideas become syllabus updates. Portfolio pack and demo day.

Outcome: Cloud agent + portfolio + demo day ready

·Cloud deploy: AWS Bedrock agents
·Cloud deploy: Azure AI Foundry SDK
·Cloud deploy: Vertex AI integration
·Innovation loop: Discuss new agent ideas live
·Innovation loop: Best ideas added to syllabus
·Innovation loop: Lifetime recordings include all updates
·Career: Capstone connected flow demo
·Career: Resume and interview prep
·Career: Demo day with Rajinikanth

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Prepare for interviews while you learn

Free AI/ML interview questions covering MLOps, LLMOps, AI Agents, FDE, and system design — written for the same roles this syllabus trains. Also review production architecture diagrams used in interviews.

Open interview questions →Architecture diagrams →

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