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Course Curriculum
GenAIOps Curriculum
Course Content
DAY 1 - Introduction to GenAIOps, Agentic AI Solutions & Environment Setup
DAY 2 - Setting Up Git, VS Code, Jupyter and UV Projects
DAY 3 - Building GenAI Applications with AWS Bedrock
DAY 3 cont...
DAY 4 - LLM Workflow, APIs and Introduction to Python
DAY 5 - Python Basics for AI Engineers
DAY 6 - Mastering Lists, Tuples and Sets in Python
DAY 7 - Advanced Python Fundamentals – Dictionaries and Functions
DAY 8 - Advanced Python Techniques and LLM Safety Mechanisms
DAY 9 - Python Programming for AI – OOP and Data Manipulation
DAY 10 - Building Data Pipelines with Pandas, Git and AWS
DAY 11 - Git Version Control and Flask Deployment on AWS EC2
DAY 12 - Flask Web Development, Git Collaboration and DVC Fundamentals
DAY 13 - Building an MLOps Pipeline with MLflow and AWS
DAY 14 - Docker, Kubernetes and Cloud Infrastructure Essentials
DAY 15 - Docker Containerization and Kubernetes Deployment on AWS
DAY 16 - Deploying Machine Learning Models with Kubernetes and KServe
DAY 17 - End-to-End MLOps Automation with Kubernetes, KServe and Argo CD
DAY 18 - Introduction to Amazon SageMaker AI and AWS MLOps
DAY 19 - Amazon SageMaker for Enterprise MLOps and Model Deployment
DAY 20 - Introduction to LangChain and Generative AI Application Development
DAY 21 - Building Autonomous Agents with LangChain and LangGraph
DAY 22 - Secure AI Agents with PII Protection and RAG Architecture
DAY 23 - Deploying AI Agents with Amazon Bedrock Agent Core
DAY 24 - Agentic Workflows, MCP and Enterprise AI Automation
DAY 25 - Prompt Engineering, Agentic Apps and System Design for AI Engineers
Course Interview Questions
GenAIOPS Interview Questions - 1
GenAIOPS Interview Questions - 2