AI Machine Learning DevOps Engineering Course
Learn AI, Machine Learning, MLOps, and DevOps Engineering through hands-on training in Python, LLM applications, model deployment, Docker, Kubernetes, CI/CD, and cloud infrastructure using industry-standard tools and real-world engineering workflows.
Course Objectives:
- Understand how AI/ML systems and DevOps pipelines are built, deployed, and managed in production environments.
- Gain hands-on experience with industry-standard AI, MLOps, and DevOps tools.
- Build, deploy, and monitor machine learning models and LLM applications.
- Automate delivery workflows using CI/CD, GitOps, and Infrastructure as Code (IaC).
- Develop practical skills for AI Engineering, MLOps, and DevOps roles.
Course Content:
Module 1: Foundations: Python, Git, and Linux
- Python programming fundamentals
- Git version control workflows
- Linux command-line operations
- Development environment setup
Module 2: Data Handling for AI/ML
- Data cleaning and transformation with Pandas and Polars
- Data preparation and feature engineering
- Building data processing pipelines
Module 3: Core Machine Learning with Scikit-Learn
- Machine learning fundamentals and workflows
- Regression and classification models
- Model training, evaluation, and optimization
- Tree-based models with XGBoost and LightGBM
Module 4: Deep Learning with PyTorch
- Neural network fundamentals
- PyTorch model development
- Training, evaluation, and deep learning workflows
Module 5: Hugging Face & Pretrained Models
- Working with pretrained AI models
- Model fine-tuning and customization
- Hugging Face ecosystem and workflows
Module 6: LLM Applications, RAG & AI Agents
- Large Language Model (LLM) applications
- LangChain and LangGraph development
- Retrieval-Augmented Generation (RAG)
- Vector databases and AI agents
Module 7: MLOps: Model Deployment & Monitoring
- Experiment tracking with MLflow
- Model serving and deployment workflows
- Performance monitoring and data drift detection
Module 8: DevOps Foundations with Docker
- Containerization concepts
- Docker images and containers
- Application packaging and deployment workflows
Module 9: Kubernetes & Container Orchestration
- Kubernetes architecture and components
- Container deployment and scaling
- Cluster management and operations
Module 10: CI/CD Automation
- Continuous Integration and Deployment workflows
- GitHub Actions and GitLab CI/CD pipelines
- Automated testing and release management
Module 11: GitOps & Infrastructure as Code
- Infrastructure management with Terraform
- GitOps workflows using Argo CD
- Automated infrastructure deployment
Module 12: Monitoring, Logging & Observability
- System monitoring with Prometheus and Grafana
- Logging and performance tracking
- Production reliability practices
Module 13: DevSecOps & Security
- Secure development practices
- Secrets management and access control
- Code and container vulnerability scanning
Module 14: Capstone Project
- Build an AI-powered application
- Containerize and deploy using CI/CD
- Monitor applications in a production environment
Course Prerequisites:
- Basic computer skills and familiarity with learning new software tools
- A laptop capable of running development tools such as Docker and a code editor (Visual Studio Code recommended)
- Basic programming knowledge is beneficial but not required, as the course begins with Python fundamentals in Module 1
International Student Fees: 1699 USD
Flexible Class Options
- Corporate Group Training | Fast-Track
- Weekend Classes For Professionals SAT | SUN
- Online Classes-Live Virtual Class(L.V.C) Online Training
