What you'll learn
Build secure production-ready LLM and Agentic AI applications using industry-leading frameworks and best practices.
Implement Guardrails, Observability, LLM Gateways, Evaluation, and Red Teaming to secure AI systems.
Develop AI agents with LangChain, LangGraph, Claude Code, Redis, and enterprise AI infrastructure tools.
Deploy scalable, reliable, monitored, and enterprise-grade AI applications with confidence in production.
Requirements
Basic knowledge of Python programming is recommended.
Familiarity with Large Language Models (LLMs) or Generative AI concepts is helpful but not mandatory.
A computer with internet access (Windows, macOS, or Linux) capable of running Python applications.
A willingness to build hands-on AI projects and learn production AI security concepts from scratch.
Description
Complete AI Security Bootcamp
Artificial Intelligence is transforming every industry, and Large Language Models (LLMs) and Agentic AI are rapidly becoming the foundation of modern software. However, as organizations move AI applications from prototypes to production, security, reliability, and governance have become just as important as model performance.
This comprehensive bootcamp is designed to teach you how to build secure, observable, and production-ready AI applications using the latest tools, frameworks, and best practices adopted across the industry.
Unlike traditional Generative AI courses that focus only on prompting or building chatbots, this course dives deep into the engineering practices required to deploy AI systems safely in real-world environments. You'll learn how to protect LLM applications against prompt injection, jailbreak attacks, data leakage, hallucinations, unsafe outputs, and other common security risks while implementing enterprise-grade monitoring, evaluation, and governance.
The course begins by covering the fundamentals of building AI applications using LangChain and LangGraph, providing a strong foundation for developing intelligent workflows and multi-agent systems. From there, you'll explore the essential pillars of Production AI Security.
You'll learn AI observability using LangSmith and Pydantic Logfire, enabling you to trace, debug, monitor, and optimize AI applications running in production. You'll understand how to identify bottlenecks, inspect agent execution paths, monitor token usage, and troubleshoot failures efficiently.
Next, you'll master AI Guardrails using industry-leading frameworks including NVIDIA NeMo Guardrails, AWS Bedrock Guardrails, and Guardrails AI. You'll learn how to enforce safety policies, validate user inputs, moderate model outputs, implement content filtering, protect against prompt injection, and build responsible AI systems suitable for enterprise deployment.
The course also explores LLM Gateways, one of the fastest-growing areas in AI infrastructure. You'll learn how to manage multiple LLM providers using Portkey AI Gateway and TensorZero, enabling intelligent model routing, request logging, authentication, fallback strategies, cost optimization, rate limiting, caching, and centralized governance.
Another major focus of the course is LLM Evaluation. You'll learn how to evaluate AI systems using DeepEval, measure answer quality, retrieval performance, hallucination rates, faithfulness, and custom evaluation metrics. You'll also explore automated testing strategies that help ensure your AI applications remain reliable as they evolve.
To strengthen your AI systems further, you'll perform AI Red Teaming using PyRIT, learning how attackers exploit vulnerabilities in LLM applications and how to defend against prompt injection, jailbreaks, malicious instructions, and unsafe behaviors before deploying to production.
Performance optimization is another critical aspect of enterprise AI. You'll learn how to integrate Redis for semantic caching, response caching, and rate limiting to improve latency, reduce operational costs, and build scalable AI services capable of handling high traffic.
Beyond securing AI applications, you'll also learn how to build modern Deep Agents and AI-powered coding assistants using Claude Code. You'll understand advanced agent architectures, planning, memory, tool calling, orchestration, and autonomous workflows used in today's production Agentic AI systems.
Throughout the bootcamp, every concept is taught through practical coding sessions, real-world examples, architecture discussions, and hands-on projects. Instead of simply learning APIs, you'll build complete AI systems that follow production engineering best practices.
What You'll Learn
- Build production-ready LLM and Agentic AI applications.
- Develop workflows using LangChain and LangGraph.
- Implement AI observability with LangSmith and Pydantic Logfire.
- Secure AI systems using NVIDIA NeMo Guardrails, AWS Bedrock Guardrails, and Guardrails AI.
- Manage multiple LLM providers using Portkey AI Gateway and TensorZero.
- Evaluate AI systems using DeepEval and custom evaluation pipelines.
- Perform AI Red Teaming with PyRIT.
- Optimize performance using Redis caching and rate limiting.
- Build advanced Deep Agents and AI coding assistants with Claude Code.
- Design scalable, secure, reliable, and enterprise-grade AI architectures.
Who this course is for
AI Engineers, ML Engineers, and Generative AI Developers who want to build secure production AI systems.
Students and professionals who want to master production AI security using modern frameworks and industry best practices.
Software Engineers and Backend Developers looking to integrate LLMs and Agentic AI into real-world applications.



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