About
The AI Engineering Bootcamp is an intensive, practical, end-to-end journey into building real, scalable AI systems. Unlike traditional courses that only teach theory, this bootcamp focuses on hands-on engineering, production pipelines, and building full AI applications from scratch using modern tools such as Python, LangChain, FastAPI, SQL, vector databases, Docker, and cloud deployment frameworks. Throughout the bootcamp, learners will work on real-world AI engineering projects, including: 🚀 Real Projects You Will Build AI News Aggregator with Intelligent Summaries Build an automated AI-powered pipeline that scrapes live news sources, clusters stories by topic, ranks relevance, and generates concise summaries using LLMs. SQL Agent for Natural Language Querying Build an LLM-driven agent that interprets natural language questions, translates them into SQL using tools and schema reasoning, executes them securely against a database, and returns clean, structured insights. Advanced RAG (Retrieval Augmented Generation) System Build an enterprise-level RAG pipeline with embeddings, vector search, document chunking strategies, rerankers, memory components, and agentic reasoning. Deploy it as an intelligent knowledge assistant with custom tools and APIs. AI Workflow Orchestration & Multi-Agent Systems Implement agents that collaborate (planner, executor, retriever, analyzer) to solve complex tasks, integrating function calling and tool execution. Production Deployment Pipeline Containerize your AI apps using Docker, build scalable FastAPI endpoints, and deploy models and agents with monitoring, logging, and evaluation tools. 🧰 Tools & Skills You'll Master Python for AI backend engineering LangChain / LlamaIndex / OpenAI tool calling Retrieval pipelines & vector databases (FAISS, Pinecone, Chroma) FastAPI for API development SQL + ORM workflows Multi-agent architectures Prompt engineering & evaluation Docker + devcontainers Cloud deployment concepts
Overview
- Understand the definitions and differences between AI and ML.
- Explore the various types of machine learning: supervised, unsupervised, and reinforcement learning.
- Discuss real-world applications of AI and ML in business.
- Learn about the data science workflow and its importance in ML projects.
- Get hands-on experience with data collection, preprocessing, and feature selection.