MLOps Engineering Training in Dindigul

Introduction

The MLOps Engineering course is a 3-month practical training program designed to teach the complete lifecycle of Machine Learning model development, deployment, automation, and monitoring. Students will learn how to integrate Machine Learning with DevOps practices to build scalable, reliable, and production-ready AI systems.

Course Syllabus

Month 1: MLOps Fundamentals & Machine Learning Basics

Introduction to MLOps

• Introduction to MLOps
• Importance of MLOps in AI Projects
• Machine Learning Lifecycle
• Data Science vs MLOps
• MLOps Workflow and Architecture
• Role and Responsibilities of MLOps Engineer
• AI Development and Production Workflow

Machine Learning Fundamentals

• Introduction to Machine Learning
• Supervised Learning
• Unsupervised Learning
• Data Collection and Preparation
• Data Preprocessing Techniques
• Feature Engineering
• Model Training and Evaluation
• Machine Learning Performance Metrics

Python for MLOps

• Python Programming Basics
• Working with NumPy and Pandas
• Data Processing and Analysis
• Machine Learning Libraries Overview
• Managing Python Environments
• Virtual Environment Setup

Version Control & Collaboration

• Introduction to Git
• GitHub Repository Management
• Git Commands and Workflow
• Branching and Merging
• Code Version Management
• Team Collaboration Practices

Month 2: Model Deployment & Automation

Machine Learning Model Deployment

• Introduction to ML Model Deployment
• Model Serialization
• Deploying Models using Flask
• FastAPI for Machine Learning APIs
• REST API Development
• Real-Time Prediction Systems
• Batch Prediction Deployment

Docker & Containerization

• Introduction to Docker
• Docker Installation and Setup
• Docker Images and Containers
• Creating Dockerfiles
• Containerizing ML Applications
• Docker Compose Basics

Kubernetes for MLOps

• Introduction to Kubernetes
• Kubernetes Architecture
• Pods and Containers
• Deployments and Services
• Scaling ML Applications
• Kubernetes Workflow for AI Systems

CI/CD Pipeline for Machine Learning

• Introduction to CI/CD
• Continuous Integration Concepts
• Continuous Deployment Concepts
• Automated ML Pipelines
• GitHub Actions
• Jenkins Basics

Month 3: Advanced MLOps & Industry Projects

MLOps Tools and Platforms

• Introduction to MLflow
• Experiment Tracking
• Model Versioning
• Model Registry
• DVC for Data Version Control
• Pipeline Automation

Cloud MLOps

• Cloud Computing Fundamentals
• AWS Machine Learning Services
• Azure Machine Learning Overview
• Google Cloud AI Platform Overview
• Cloud-Based Model Deployment
• Scalable AI Infrastructure

Model Monitoring & Maintenance

• ML Model Monitoring Concepts
• Performance Tracking
• Data Drift Detection
• Model Retraining Strategies
• Logging and Error Monitoring
• AI System Maintenance

Advanced MLOps Practices

• End-to-End ML Pipeline Development
• Production ML Architecture
• Automation Best Practices
• Security in MLOps
• AI Governance
• Industry MLOps Workflow

Final Projects

• End-to-End Machine Learning Deployment Project
• ML Model API Development
• Automated ML Pipeline Project
• Cloud-Based AI Application
• Real-Time Model Monitoring System

Tools and Technologies

• Python
• Machine Learning
• Git & GitHub
• Docker
• Kubernetes
• MLFLOW
• DVC
• Jenkins
• GitHub Actions
• Flask
• FASTAPI
• AWS
• Azure
• Google Cloud Platform