Prerequisites & System Requirements Programming & Foundations: Working knowledge of Python or JavaScript, Git, and the command line. AI & LLM Basics: A foundational understanding of LLMs, prompts, and APIs (prior familiarity with RAG and agents is helpful). Cloud & Hardware: A laptop, an Android phone (for the on-device mobile project), and a cloud account (AWS, GCP, or Azure) with billing enabled for GPU labs. Coding Agents: A paid plan for at least one AI coding agent (such as Claude Code, Cursor, or OpenAI Codex) is recommended. Everyone is building with AI, but very few are shipping it. At Krish Naik Academy, we designed the AI System Design Bootcamp to help engineers bridge the gap between running local prototypes and deploying scalable, monetizable AI applications. If you are ready to move beyond simple weekend projects, we will take you step-by-step through designing, deploying, and scaling production AI systems across the application, AI, data, and infrastructure layers. Detailed Curriculum Structure Part 1: Modern AI Development and Agents: Covers the modern AI-native Software Development Life Cycle (SDLC) using spec-driven development, PRDs, and advanced utilisation of coding agents like Claude Code, Cursor, and OpenAI Codex. Students also build and govern always-on AI agent fleets using an AgentOps control tower. Part 2: Inference Engineering: Focuses on optimising LLM speed and affordability. Students learn to size GPUs, benchmark engines (like VLLM and SGLang), and reduce latency through batching, caching, and quantisation. Part 3: AI Infrastructure and Enterprise AI: Involves self-hosting and fine-tuning open-source LLMs on company infrastructure, deploying LLMs locally on mobile devices (Android and iOS) with cloud fallbacks, and designing multimodal Retrieval-Augmented Generation (RAG) systems at scale. Part 4: Real-World AI Products: Students build, package, and launch production-grade systems, including an enterprise lead management system, a localised AI voice receptionist, and a desktop voice dictation app launched as a paid SaaS with subscriptions Graduates will complete the program with a portfolio of six deployed, monitored, and secured production systems. The bootcamp prepares students for advanced roles such as AI Engineer, AI System Designer, Inference Engineer, and AI Solutions Architect.

Live

Oct 4, 2026

AI Forward Deployed Engineer Bootcamp

Build Production-Ready Enterprise AI Systems Master the skills to become an AI Forward Deployed Engineer—from software engineering and AWS deployment to RAG, Knowledge Graphs, Multimodal AI, Agentic AI, enterprise integrations, security, and observability Build real-world enterprise AI solutions using LangGraph, MCP, FastAPI, Docker, AWS, Neo4j, Pinecone, and more with two end-to-end enterprise capstone projects. Prerequisites: Basic knowledge of Python, SQL, APIs, and Git with a strong problem-solving mindset. No prior experience in GenAI, RAG, Agents, Cloud, or DevOps is required. Learn. Build. Deploy. Secure. Scale AI for the enterprise.

3.0 Ultimate Data Science & Ai Engineering

Live

Sep 6, 2026

3.0 Ultimate Data Science & Ai Engineering

Zero prerequisites, one ultimate course: go from absolute beginner to building and deploying production-ready data science and AI solutions. This course is a comprehensive journey through modern Data Science and AI, designed to take learners from Python programming basics all the way to next-generation Agentic AI systems. You will begin by mastering Python, statistics, and core machine learning techniques, then progress into deep learning, generative AI, RAG, and model lifecycle management with MLflow. By the end of this year-long program, you will understand how the world is moving towards autonomous AI systems that reason, retrieve, and collaborate.

Udemy Success

900,000+ Students Learning on Udemy

With over 900,000 students enrolled and a 4.8-star rating, Krish Naik's Udemy courses are among the most trusted resources for learning Data Science, Machine Learning, and AI.