MLOps & AIOps Masterclass
DevOps to Production AI Systems

🏆 #1 Rated Course
⭐ 4.9/5 Rating
🔥 500+ Enrolled

Transform Your Career with Industry-Ready MLOps, AIOps, LLMOps & AI Agent Development Skills. Build Production-Ready ML Systems with Docker, Kubernetes, MLflow, Kubeflow, and LangChain.

12-16
Weeks Duration
200+
Hours Training
50+
Lab Exercises
4
Capstone Projects
60%
Avg Salary Hike
Enroll Now → 💬 WhatsApp ▶️ YouTube

✓ Live Online Classes ✓ Hands-on Labs ✓ 1-on-1 Mentorship ✓ Job Assistance ✓ Lifetime Access

COURSE OVERVIEW

What You'll Master

Complete lifecycle from experimentation to production AI systems

🐳

DevOps for ML/AI

Docker, Kubernetes, CI/CD, Terraform, Ansible - infrastructure for AI workloads.

🔄

MLOps Pipelines

End-to-end ML pipelines, MLflow, Kubeflow, model versioning, deployment.

🧠

LLMOps & RAG

Deploy LLMs, fine-tuning, RAG systems, vector databases, prompt engineering.

⚡

AIOps Automation

Anomaly detection, predictive analytics, self-healing infrastructure.

🤖

AI Agents

LangChain, autonomous agents, tool use, multi-agent systems.

☁️

Multi-Cloud

AWS SageMaker, Azure ML, GCP Vertex AI - production deployment.

1

DevOps Fundamentals for MLOps

Master essential DevOps skills required for ML/AI operations

Linux & Shell Scripting

  • Linux commands for ML workflows
  • Bash scripting for automation
  • File system & process management
  • Network configuration

Git & Version Control

  • Git workflow best practices
  • Branching strategies for ML
  • Pull requests & collaboration
  • Git hooks for automation

Docker Containerization

  • Docker fundamentals
  • Dockerfile best practices for ML
  • Container networking & volumes
  • Docker Compose
🔧 HANDS-ON: Containerizing ML Models

Kubernetes for ML

  • K8s architecture & components
  • Pods, Deployments, Services
  • ConfigMaps & Secrets
  • Persistent volumes for ML data
🔧 HANDS-ON: Deploying ML on K8s

Infrastructure as Code

  • Terraform for cloud infrastructure
  • Ansible for configuration
  • CloudFormation & ARM
🔧 HANDS-ON: Automating ML Infra

CI/CD Pipelines

  • Jenkins setup & pipelines
  • GitHub Actions for ML
  • Automated testing & deployment

Cloud Computing

  • AWS, Azure, GCP comparison
  • SageMaker, Vertex AI, Azure ML
  • Cost optimization strategies

Monitoring & Observability

  • Prometheus & Grafana
  • ELK/EFK Stack
  • Distributed tracing
🔧 HANDS-ON: Building Dashboards
2

MLOps - Machine Learning Operations

Build automated, scalable ML pipelines from experimentation to production

MLOps Fundamentals

  • ML lifecycle & maturity model
  • MLOps vs DevOps differences
  • Industry best practices
  • Common deployment challenges

Data Engineering for ML

  • Data ingestion & validation
  • Feature engineering at scale
  • Feature stores (Feast)
  • Data quality monitoring
🔧 HANDS-ON: Apache Airflow Pipelines

Experiment Tracking

  • MLflow for tracking
  • Weights & Biases
  • Hyperparameter optimization
  • Reproducible research
🔧 HANDS-ON: Experiment Tracking System

Model Versioning

  • Code versioning with Git
  • Data versioning with DVC
  • Model registry strategies
  • Artifact management
🔧 HANDS-ON: Complete Versioning Workflow

Model Deployment

  • Model serialization (ONNX, SavedModel)
  • RESTful API with FastAPI
  • Batch inference systems
  • Model serving architectures
🔧 HANDS-ON: Deploy as Microservices

ML CI/CD Pipelines

  • Automated testing for ML
  • Continuous training & deployment
  • ML-specific CI/CD challenges
  • Pipeline orchestration
🔧 HANDS-ON: End-to-End ML CI/CD

Model Monitoring

  • Performance metrics
  • Data drift detection
  • Concept drift detection
  • A/B testing for ML
🔧 HANDS-ON: Drift Detection System

ML Orchestration

  • Kubeflow Pipelines
  • AWS SageMaker Pipelines
  • Azure ML Pipelines
  • Vertex AI Pipelines
🔧 HANDS-ON: Complex ML Workflows
3

LLMOps - Large Language Model Operations

Deploy, manage, and optimize Large Language Models in production

LLMOps Fundamentals

  • LLM lifecycle management
  • Open-source vs proprietary (GPT, LLaMA, Claude)
  • Deployment challenges
  • Cost optimization

Prompt Engineering

  • Prompt patterns & techniques
  • Prompt versioning & templates
  • Testing frameworks
🔧 HANDS-ON: Prompt Management System

LLM Fine-tuning

  • Fine-tuning methodologies
  • LoRA and QLoRA techniques
  • Domain adaptation
  • Evaluation metrics
🔧 HANDS-ON: Fine-tune Open-Source LLM

RAG Systems

  • RAG architecture
  • Vector DBs (Pinecone, Weaviate, ChromaDB)
  • Document chunking & embeddings
  • Hybrid search
🔧 HANDS-ON: Production RAG System

LLM Deployment

  • Inference optimization
  • Quantization & compression
  • Caching strategies
  • Load balancing & scaling
🔧 HANDS-ON: Deploy Optimized LLM

LLM Monitoring

  • Output quality monitoring
  • Response time & cost tracking
  • Automated evaluation
  • User feedback integration
🔧 HANDS-ON: LLM Observability

Responsible AI & Guardrails

  • Content filtering
  • Bias detection
  • Explainability
  • Compliance & governance
🔧 HANDS-ON: Safety Guardrails
4

AIOps - AI for IT Operations

Automate IT operations with AI-powered monitoring, prediction, and remediation

AIOps Fundamentals

  • AIOps concepts & maturity
  • Business value & ROI
  • vs Traditional IT Ops
  • Implementation strategies

IT Data Collection

  • Telemetry & metrics
  • Log aggregation
  • APM integration
  • Data frameworks
🔧 HANDS-ON: Data Pipelines

Anomaly Detection

  • Statistical methods
  • ML-based detection
  • Time-series analysis
  • Multivariate analysis
🔧 HANDS-ON: Anomaly Detection Models

Predictive Analytics

  • Failure prediction
  • Capacity planning
  • SLA optimization
  • Resource forecasting
🔧 HANDS-ON: Predictive Maintenance

Root Cause Analysis

  • Automated RCA
  • Event correlation
  • Pattern recognition
  • Causal inference
🔧 HANDS-ON: Automated RCA System

Self-Healing Infrastructure

  • Auto-remediation patterns
  • Self-healing infra
  • Chaos engineering
  • Resilience testing
🔧 HANDS-ON: Self-Healing System

Cloud-Native AIOps

  • Kubernetes observability
  • Microservices health
  • Serverless monitoring
  • Container intelligence
🔧 HANDS-ON: Cloud-Native AIOps
5

AI Agents & Autonomous Systems

Build intelligent AI agents that can reason, plan, and take autonomous actions

AI Agent Fundamentals

  • Agent architecture
  • Types of AI agents
  • Business applications
  • Ethical considerations

Agent Frameworks

  • LangChain development
  • LlamaIndex for retrieval
  • AutoGPT patterns
  • CrewAI multi-agent
🔧 HANDS-ON: Build First AI Agent

Tool Use & Function Calling

  • Function calling architecture
  • Tool libraries & APIs
  • Tool selection logic
  • Custom tool development
🔧 HANDS-ON: Tool-Enabled Agents

Agent Memory

  • Short & long-term memory
  • Vector DB for memory
  • Memory retrieval
  • Knowledge graphs
🔧 HANDS-ON: Persistent Agent Memory

Planning & Reasoning

  • Chain-of-thought
  • Tree of thought
  • Task decomposition
  • Goal-oriented behavior
🔧 HANDS-ON: Reasoning Systems

Multi-Agent Systems

  • Multi-agent architectures
  • Communication protocols
  • Role specialization
  • Collaborative solving
🔧 HANDS-ON: Multi-Agent Workflows

Agent Testing

  • Evaluation frameworks
  • Simulation environments
  • Adversarial testing
  • Performance optimization
🔧 HANDS-ON: Agent Capabilities

Enterprise Deployment

  • Security considerations
  • Scalable infrastructure
  • Monitoring & governance
  • Human-in-the-loop
🔧 HANDS-ON: Production Agents
MODULE 6

Real-World Capstone Projects

Build complete, production-ready systems for your portfolio

🚀 Project 1: End-to-End MLOps Pipeline

Build automated ML pipeline with CI/CD, deploy to Kubernetes, implement monitoring and drift detection, set up automated retraining.

Python MLflow Docker Kubernetes Jenkins

🧠 Project 2: Production LLM Application with RAG

Fine-tune open-source LLM, build RAG system with vector database, implement prompt management, deploy with monitoring.

LangChain ChromaDB FastAPI Docker HuggingFace

⚡ Project 3: AIOps Monitoring System

Build anomaly detection system, implement predictive maintenance, create automated remediation workflows, integrate with ITSM.

Prometheus Grafana Python Kubernetes Scikit-learn

🤖 Project 4: Enterprise AI Agent

Build multi-agent system for business tasks, implement tool integration, create human-in-the-loop workflows, deploy with security.

LangChain CrewAI FastAPI PostgreSQL Docker
WHAT'S INCLUDED

Complete Learning Package

📹

Live Interactive Classes

Weekly live sessions with Q&A

⏱️

200+ Hours Training

Comprehensive hands-on learning

🔬

50+ Lab Exercises

Real-world practical labs

🏆

4 Capstone Projects

Portfolio-ready projects

👨‍🏫

1-on-1 Mentorship

Personal guidance sessions

📝

Resume Optimization

LinkedIn & resume review

🎯

Interview Prep

Mock interviews & guidance

💼

Job Assistance

Career support & placement

STUDENT SUCCESS

What Our Students Say

★★★★★

"This course transformed my career from Data Scientist to MLOps Engineer. The hands-on projects gave me the confidence to handle production ML systems. Landed 40% salary hike!"

RK
Rahul K.
MLOps Engineer @ Top MNC
★★★★★

"Best investment I made in 2025. The AI Agents module was amazing - cutting edge content you won't find anywhere else. Got placed as ML Engineer within 2 months!"

PS
Priya S.
ML Engineer @ Startup
★★★★★

"Rajinikanth sir explains complex MLOps concepts so simply. The Kubernetes and Docker sections were game-changers. Now I deploy ML models like a pro!"

AV
Amit V.
DevOps → MLOps Engineer
CAREER OPPORTUNITIES

Roles You'll Be Ready For

MLOps Engineer

₹12-35 LPA

ML Engineer

₹15-40 LPA

AIOps Engineer

₹12-30 LPA

LLM Engineer

₹20-50+ LPA

AI Agent Developer

₹18-45 LPA

ML Platform Engineer

₹18-40 LPA

SRE (ML Focus)

₹15-35 LPA

DevOps (AI/ML)

₹12-30 LPA

🌍 Global Salaries: USA: $120K-$200K+ | Europe: €70K-€120K+

Transform Your Career in AI/ML Engineering!

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Student Success Stories

See how our students transformed their careers with hands-on training

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