This course is designed to take you from automation basics to building production-ready autonomous AI agents using industry-leading tools and frameworks.
🔗 Workflow Automation with n8n
Design and build powerful automation workflows
Connect APIs, databases, and AI models seamlessly
Create real-world business automations without heavy coding
🧑💻 Version Control with Git & GitHub
Git fundamentals: commits, branches, merges
Collaborative development using GitHub
Manage AI and automation projects professionally
🧠 LLM Application Development with LangChain
Build intelligent LLM-powered applications
Prompt templates, chains, memory, and tools
Integrate LLMs with external data and APIs
⚡ Production APIs with LangServe
Deploy LangChain applications as scalable APIs
Serve AI workflows securely and efficiently
Prepare applications for real-world usage
🔍 Observability & Debugging with LangSmith
Trace and debug LLM executions
Monitor prompts, latency, and costs
Improve reliability and performance of AI agents
🧩 Multi-Agent Systems with LangGraph
Build stateful and multi-step AI workflows
Design agent decision flows and control logic
Create robust agent architectures
🤖 Autonomous Agents with CrewAI
Build multi-agent AI teams with defined roles
Agent collaboration, planning, and execution
Solve complex tasks using agent coordination
☁️ Cloud Deployment with AWS
Deploy Agentic AI applications on the cloud
Use AWS services for scalability and security
Understand cloud architecture for AI systems
🐳 Containerization with Docker
Containerize AI and automation applications
Ensure portability across environments
Deploy and scale agent systems efficiently
🏗️ End-to-End Agentic AI Projects
Build real-world Agentic AI solutions
Automate workflows, decision-making, and execution
