PROGRAMME CURRICULUM

Build End-to-End Expertise with a Structured Curriculum

Learn cloud infrastructure, DevOps practices, and modern deployment workflows through a complete learning path.

Module 1: Linux & Networking Foundations

Work with Linux systems, commands, users, and processes
Manage packages, logs, and system monitoring
Understand file systems, permissions, and shell usage
Learn networking fundamentals including TCP/IP and OSI model
Configure IP addressing, DNS, and subnetting
Troubleshoot networks and system-level issues

Module 3: Programming & Scripting

Write and execute shell scripts for automation
Work with variables, conditions, loops, and functions
Debug scripts and manage execution workflows
Understand API interaction for DevOps use cases
Use Python for automation and DevOps tasks

Module 5: Containers (Docker)

Understand containerization concepts and Docker architecture
Build, manage, and optimize Docker images and containers
Work with container networking and storage
Develop and manage Dockerfiles and multi-container setups
Implement container security and lifecycle management

Module 7: CI/CD

Understand CI/CD concepts and pipeline design
Automate build, test, and deployment workflows
Manage pipeline stages, artifacts, and integrations
Implement container-based pipelines
Secure and monitor CI/CD pipelines

Module 9: Configuration Management

Understand configuration management concepts
Automate server provisioning and infrastructure setup
Manage inventory, roles, and playbooks
Enforce configurations across environments
Automate application deployment workflows

Module 11: Observability

Understand observability concepts: metrics, logs, and tracing
Monitor applications and infrastructure performance
Implement logging and distributed tracing
Build dashboards and visualizations
Configure alerting and monitoring systems

Module 13: AI for DevOps Engineering

Understand AI foundations and AI-assisted DevOps workflows
Automate infrastructure and CI/CD processes using AI-powered tools
Implement AI-driven deployment, observability, and monitoring practices
Apply AIOps concepts for system analysis and operational efficiency
Strengthen DevSecOps and cloud security workflows using AI tools

Module 2: Git & Version Control

Understand version control systems and Git fundamentals
Manage repositories, commits, and branching strategies
Work with collaboration workflows and code reviews
Handle pull requests and Git-based development workflows
Secure repositories and manage access control

Module 4: Cloud Fundamentals (AWS)

Understand cloud computing concepts and AWS architecture
Work with compute, storage, networking, and database services
Manage IAM, security, and access control
Implement DNS, content delivery, and serverless services
Monitor and manage cloud infrastructure and logs

Module 6: Kubernetes & Cloud Native

Understand Kubernetes architecture and cluster setup
Work with core objects, services, and networking
Manage scaling, storage, and configuration
Deploy applications using Kubernetes workflows
Handle package management and security
Work with managed Kubernetes services

Module 8: Infrastructure as Code

Understand IaC concepts and declarative infrastructure
Provision infrastructure using Terraform
Work with modules, configurations, and templates
Manage infrastructure state and validation
Automate infrastructure provisioning workflows

Module 10: GitOps

Understand GitOps principles and workflows
Manage Git-driven infrastructure and deployments
Implement continuous delivery using GitOps models
Handle rollbacks, reconciliation, and environment management
Work with Kubernetes-based deployment workflows

Module 12: DevSecOps

Understand DevSecOps principles and secure development
Perform code analysis and vulnerability scanning
Implement container and image security
Manage secrets and compliance workflows
Secure infrastructure and monitor threats

Module 1: Linux & Networking Foundations

Work with Linux systems, commands, users, and processes
Manage packages, logs, and system monitoring
Understand file systems, permissions, and shell usage
Learn networking fundamentals including TCP/IP and OSI model
Configure IP addressing, DNS, and subnetting
Troubleshoot networks and system-level issues

Module 2: Git & Version Control

Understand version control systems and Git fundamentals
Manage repositories, commits, and branching strategies
Work with collaboration workflows and code reviews
Handle pull requests and Git-based development workflows
Secure repositories and manage access control

Module 3: Programming & Scripting

Write and execute shell scripts for automation
Work with variables, conditions, loops, and functions
Debug scripts and manage execution workflows
Understand API interaction for DevOps use cases
Use Python for automation and DevOps tasks

Module 4: Cloud Fundamentals (AWS)

Understand cloud computing concepts and AWS architecture
Work with compute, storage, networking, and database services
Manage IAM, security, and access control
Implement DNS, content delivery, and serverless services
Monitor and manage cloud infrastructure and logs

Module 5: Containers (Docker)

Understand containerization concepts and Docker architecture
Build, manage, and optimize Docker images and containers
Work with container networking and storage
Develop and manage Dockerfiles and multi-container setups
Implement container security and lifecycle management

Module 6: Kubernetes & Cloud Native

Understand Kubernetes architecture and cluster setup
Work with core objects, services, and networking
Manage scaling, storage, and configuration
Deploy applications using Kubernetes workflows
Handle package management and security
Work with managed Kubernetes services

Module 7: CI/CD

Understand CI/CD concepts and pipeline design
Automate build, test, and deployment workflows
Manage pipeline stages, artifacts, and integrations
Implement container-based pipelines
Secure and monitor CI/CD pipelines

Module 8: Infrastructure as Code

Understand IaC concepts and declarative infrastructure
Provision infrastructure using Terraform
Work with modules, configurations, and templates
Manage infrastructure state and validation
Automate infrastructure provisioning workflows

Module 9: Configuration Management

Understand configuration management concepts
Automate server provisioning and infrastructure setup
Manage inventory, roles, and playbooks
Enforce configurations across environments
Automate application deployment workflows

Module 10: GitOps

Understand GitOps principles and workflows
Manage Git-driven infrastructure and deployments
Implement continuous delivery using GitOps models
Handle rollbacks, reconciliation, and environment management
Work with Kubernetes-based deployment workflows

Module 11: Observability

Understand observability concepts: metrics, logs, and tracing
Monitor applications and infrastructure performance
Implement logging and distributed tracing
Build dashboards and visualizations
Configure alerting and monitoring systems

Module 12: DevSecOps

Understand DevSecOps principles and secure development
Perform code analysis and vulnerability scanning
Implement container and image security
Manage secrets and compliance workflows
Secure infrastructure and monitor threats

Module 13: AI for DevOps Engineering

Understand AI foundations and AI-assisted DevOps workflows
Automate infrastructure and CI/CD processes using AI-powered tools
Implement AI-driven deployment, observability, and monitoring practices
Apply AIOps concepts for system analysis and operational efficiency
Strengthen DevSecOps and cloud security workflows using AI tools

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Module 11

Advanced Machine Learning

Advanced Classification Techniques
Sophisticated Clustering Methods
Dimensionality Reduction
Support Vector Machines (SVM)
Model Evaluation & Improvement
Ensemble Techniques
Reinforcement Learning

Module 12

Introduction to MLOps: Bridge the gap between Machine Learning and real-world deployment

What is MLOps? Why is it important?
ML lifecycle: from experimentation to deployment
Key differences between DevOps and MLOps
Components of the MLOps ecosystem
Tools and platforms overview
Model versioning and tracking
Creating reproducible ML workflows
Data version control and pipeline automation
Introduction to containers (Docker) for ML reproducibility
Deployment strategies
Infrastructure options: cloud, on-prem, hybrid
Monitoring models in production: drift, bias, accuracy
Collaboration across teams (Dev, Data Science, Ops)
CI/CD pipeline basics for ML projects
Security and ethical considerations in ML deployments
Hands-on demo

Module 13

Introduction to Neural Networks

What are neural networks and how they work
Biological inspiration vs artificial implementation
Structure of a neural network: neurons, layers, weights, and biases
Activation functions: ReLU, sigmoid, tanh, softmax
Forward propagation and loss calculation
Backpropagation and the learning process
Gradient descent and optimization techniques
Introduction to deep learning frameworks: TensorFlow and PyTorch
Building a simple neural network from scratch
Visualizing the training process and performance metrics
Overfitting and regularization techniques
Real-world applications of neural networks
Key terminology: epochs, batches, learning rate, accuracy

Module 14

Deep Neural Networks

Understanding the architecture of Deep Neural Networks (DNNs)
Benefits of depth: capturing complex patterns and hierarchies
Hidden layers: how many, how deep, and why it matters
Training challenges in deep networks
Optimization strategies
Building and training DNNs with TensorFlow/Keras
Activation functions in deep networks
Monitoring performance: loss curves, accuracy tracking
Model evaluation and validation techniques
Using callbacks and checkpoints
Real-world DNN applications and case studies
Best practices for scaling deep models

Module 15

Applied Deep Learning with PyTorch

Introduction to PyTorch: tensors, automatic differentiation, and the computation graph
PyTorch vs other frameworks: why researchers and developers prefer it
Building neural networks using torch.nn and nn.Module
Implementing forward passes and activation functions
Loss functions and optimizers using torch.optim
Writing custom training and validation loops
Efficient data loading using torch.utils.data.Dataset and DataLoader
Handling overfitting with dropout, regularization, and early stopping
Visualizing metrics and loss curves with TensorBoard
Saving and loading models using state_dict and checkpoints
Introduction to Intel Arc GPU architecture and AI acceleration
Setting up PyTorch with Intel® Extension for PyTorch (IPEX)
Training models on Intel Arc GPU
Case study: Training an image classifier on Intel Arc GPU
Best practices for deploying models trained with PyTorch

Module 16

Introduction to Computer Vision with CNNs

Introduction to Computer Vision and real-world applications
Challenges in interpreting images through AI
Image representation: pixels, channels, resolution, and color spaces
Why CNNs? Understanding the limitations of traditional neural networks for vision tasks
Fundamentals of Convolutional Neural Networks (CNNs)
Building a simple CNN architecture from scratch
Image classification workflow using TensorFlow/Keras or PyTorch
Working with image datasets (CIFAR-10, MNIST, etc.)
Data preprocessing and augmentation techniques
Model training, validation, and evaluation
Visualization of learned filters and feature maps
Transfer learning and using pre-trained CNNs (VGG, ResNet, MobileNet)
Performance tuning and avoiding overfitting in CNNs
Hands-on: Building a digit/image classifier using CNN
Introduction to edge deployment on AI-enabled devices

Module 17

Natural Language Processing (NLP)

Introduction to NLP and its real-world applications
Understanding structured vs unstructured text data
Text preprocessing techniques
Working with libraries like NLTK and spaCy
Text representation methods
Word embeddings
Text classification basics
Sequence models overview
Hands-on: Building a sentiment analysis classifier
Evaluating NLP models: accuracy, precision, recall, F1-score
Introduction to transfer learning in NLP
Ethical challenges in NLP: bias and misinformation

Module 18

Introduction to Generative AI

What is Generative AI?
Real-world applications of generative models
Generative vs traditional AI: key differences
Overview of foundational models
Understanding autoencoders and latent spaces
Introduction to Generative Adversarial Networks (GANs)
Introduction to Diffusion Models
Introduction to Large Language Models (LLMs)
Creative tools powered by Gen AI
Ethical considerations in content generation
Understanding hallucinations and content validation
The role of prompt engineering in content control
Hands-on demo
Introduction to open-source generative AI tools
The future of Generative AI and emerging trends

Module 19

Large Language Models (LLMs) and Prompt Engineering

Introduction to LLMs: what they are and how they work
Overview of popular models: GPT, LLaMA,Claude, Mistral, and Gemini
LLM capabilities and limitations
Tokenization and model context window
Few-shot, zero-shot, and chain-of-thought prompting
Prompt Engineering fundamentals
Prompt patterns for specific tasks
Using OpenAI’s GPT and HuggingFace models via APIs
Tools for prompt testing and refinement
Introduction to prompt chaining and dynamic prompt templates
Safety, alignment, and ethical concerns when working with LLMs
Hands-on: Crafting and refining prompts for real-world tasks

Module 20

Building AI-Powered Applications with Flask and Streamlit

Introduction to web applications for AI and ML
Overview: Flask vs Streamlit – use cases and strengths
Basics of Flask
Basics of Streamlit
Structuring a basic AI application
Integrating pre-trained ML/LLM models into apps
Connecting front-end inputs to back-end predictions
Uploading files and handling user input
Hosting local vs cloud-based applications
Debugging and testing application workflows
Preparing apps for deployment (Heroku, Render, or cloud)
UX tips for building user-friendly AI interfaces

Module 21

Advanced Prompt Engineering and LLM Fine-Tuning

Beyond the basics: Prompt refinement strategies for precision
System prompts and role-based persona engineering
Few-shot learning with structured examples
Chain-of-thought prompting and multi-step reasoning
Embedding-based prompts and semantic similarity
Prompt chaining for multi-turn, multi-step tasks
Retrieval-augmented prompting vs vanilla prompting
Controlling tone, length, style, and response format
Function calling and tool use with LLMs (OpenAI, Claude)
Limitations of prompting and when fine-tuning is needed
Introduction to fine-tuning LLMs
Datasets for fine-tuning (custom, open-source)
Using Hugging Face tools to fine-tune a model (e.g., LLaMA, Mistral)
Evaluating fine-tuned models: performance and generalization
Best practices for safe and ethical deployment

Module 22

Reinforcement Learning: Fine-tune generative models through feedback-driven learning

Overview of RL in the context of Generative AI
What is Reinforcement Learning from Human Feedback (RLHF)?
Role of feedback loops in fine-tuning LLM behavior
Architectures that support RLHF (e.g., PPO, DPO)
Stages of RLHF
Algorithms used in RLHF
Designing reward models for generative tasks
Aligning language models with ethical guidelines and safety constraints
Case study: How OpenAI uses RLHF in ChatGPT
RLHF vs supervised fine-tuning – when to use what
Challenges in scaling RL for large models
Bias, interpretability, and feedback reliability
Hands-on example: Use a small-scale transformer with simulated feedback
Tools and libraries: TRL (Hugging Face), Accelerate, RLlib

Module 23

Retrieval-Augmented Generation (RAG) for AI Models

What is Retrieval-Augmented Generation (RAG) and why it matters
Key components of a RAG pipeline
Vector databases: FAISS, Pinecone, Weaviate overview
Document chunking strategies for optimal retrieval
Text embeddings and semantic search
Creating knowledge bases from PDFs, HTML, Notion, etc.
Connecting LLMs with retrieval layers using LangChain
Prompting with contextual information from retrievers
RAG vs traditional search vs fine-tuning: when to use what
Deploying a basic RAG-powered Q&A app
Evaluation metrics: relevancy, latency, hallucination reduction
Best practices for scaling RAG systems
Privacy and compliance in RAG-powered enterprise AI
Case studies: RAG in customer support, internal documentation bots

Module 24

Building a Local Retrieval-Augmented Generation (RAG) System on Intel AI PC

Setting Up the Intel AI PC Environment
Developing the RAG Pipeline
Optimization Techniques for Intel Hardware
Testing and Evaluation of the RAG System
Deployment and Maintenance
Hands on: Building a local RAG based application

Module 25

Agentic AI – Autonomous AI Systems

What is Agentic AI and how it differs from traditional AI models
Use cases of AI agents in research, productivity, business, and automation
Core components of an AI agent: memory, planning, reasoning, and tool use
Frameworks for building agents
Agent architecture and task decomposition
Integrating tools and APIs into agent workflows
ReAct (Reasoning + Acting) and other planning strategies
Short-term vs long-term memory in agent systems
Using vector databases and document loaders as knowledge tools
Creating multi-agent systems with specialized roles
Orchestration of concurrent agents for complex goals
Logging, monitoring, and controlling autonomous behavior
Security, constraints, and ethical boundaries in autonomous AI
Hands-on: Build a research assistant or data analyst AI agent
Future directions: self-improving and recursive agents

Module 26

Setting up Docker for AI Development

Introduction to Docker
Installing and Configuring Docker
Creating a Containerized AI Development Environment
Running and Managing ContainersRunning and Managing Containers
Docker Compose for Multi-Service AI Projects
Best Practices

Module 27

Cloud Deployment for LLM-Based Applications

Introduction to cloud computing: key concepts and benefits
Overview of major platforms: AWS, Azure, GCP, and Intel Developer Cloud
Cloud infrastructure essentials: compute, storage, networking
Hosting and deploying containerized AI apps with Docker
Using cloud services to run FastAPI or Streamlit apps
Deploying LLM APIs (e.g., OpenAI, Hugging Face) via cloud functions or REST endpoints
Environment setup and configuration
Introduction to serverless computing (Lambda, Azure Functions, Google Cloud Functions)
CI/CD pipelines for AI app updates
Working with secrets, environment variables, and model endpoints securely
Logging, monitoring, and scaling applications in the cloud
Using GPU instances to run inference with large models
Cost management and optimization tips
Hands-on: Deploy a simple LLM-powered chatbot or document Q&A system on the cloud
Bonus: Introduction to Intel Developer Cloud for AI acceleration

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Step into High-Growth Careers in Cloud & DevOps

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Build End-to-End Expertise with a Structured Curriculum

Module 1: Linux & Networking Foundations

Module 3: Programming & Scripting

Module 5: Containers (Docker)

Module 7: CI/CD

Module 9: Configuration Management

Module 11: Observability

Module 13: AI for DevOps Engineering

Module 2: Git & Version Control

Module 4: Cloud Fundamentals (AWS)

Module 6: Kubernetes & Cloud Native

Module 8: Infrastructure as Code

Module 10: GitOps

Module 12: DevSecOps

Module 1: Linux & Networking Foundations

Module 2: Git & Version Control

Module 3: Programming & Scripting

Module 4: Cloud Fundamentals (AWS)

Module 5: Containers (Docker)

Module 6: Kubernetes & Cloud Native

Module 7: CI/CD

Module 8: Infrastructure as Code

Module 9: Configuration Management

Module 10: GitOps

Module 11: Observability

Module 12: DevSecOps

Module 13: AI for DevOps Engineering

Transforming Careers with Cloud and DevOps

Upskill With Guidance From Expert Mentors

Build with the Tools Powering Modern DevOps

Turn Your Skills into Projects That Stand Out

Earn a Certification That Speaks for Your Skills

Crack Interviews with End-to-End Placement Support

How the Programme Works

Explore an interactive step-by-step journey.

Guided onboarding and basics

Expert-Led Structured modules

Hands-On Practice

Industry Tool Mastery

Portfolio and interview prep

How the Programme Works

Explore an interactive step-by-step journey.

Guided onboarding and basics

Expert-Led Structured modules

Hands-On Practice

Industry Tool Mastery

Portfolio and interview prep

Invest in Your Future with Flexible Pricing

Per month For 12 Month

Per month For 12 Month

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