MLOps Engineering Online Live Class by Experts
Our Training/Internship Process
MLOps Engineering - Syllabus, Fees & Duration
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
This syllabus is not final and can be customized as per needs/updates
- Since 2001, Making IT Experts by Experts
- Duration: 100 Hours (Vary as per your skill)
- Course Fees
- Individual Live Classes (1:1)
- Batch Classes @ Low Fees
- Training on Your Time, Any Where
- Access to Recorded Videos
- Practical Internship on Projects
- 100% Placement Support by our jobsHybrid.com
- Training/Internship Certificate
Course Highlights
**Course Description (Use Hamilton)**
Our **MLOps Engineering Course in Hamilton** is a 3-month advanced training program designed to teach the complete lifecycle of Machine Learning operations, including model development, deployment, automation, and monitoring.
. Students will learn how to combine Machine Learning and DevOps practices to build scalable, secure, and production-ready AI applications.
This course provides hands-on training in ML model deployment, Docker, Kubernetes, CI/CD pipelines, cloud platforms, model versioning, and MLOps tools used by industries. Join our **MLOps Engineering Training in Hamilton** to develop industry-ready skills and build efficient AI systems for modern technology environments. 
