AI & Machine Learning Engineering Bootcamp

In the first phase students will become familiar with software engineering practices and how they relate to data science. The objective of the first week is to write better code when working with data science projects. In order to achieve this we will cover software engineering in Python (writing programs, working with git and object-oriented programming). Then we will show how to bridge the gap between the usual data science workflow and production-ready code.

At the end of this phase students will be comfortable with getting data for their models from many different sources in different formats. Data engineering is about moving and transforming data from one place to another in a reliable and trustworthy way. Students will get introduced to data architecture design for batch and real-time data processing. They will learn how to get data from various sources like database access and APIs. They will then learn the concepts of data modeling with DBT. Following that they will build data pipelines with Prefect and learn the concepts of batch processing and streaming. Finally, they will set up a feature engineering pipeline in the cloud for their Data science project.

In the third phase of the bootcamp the students will get familiar with the machine learning lifecycle and how to bring data science products to production. There will be an introductory session on machine learning basics followed by sessions on testing, deployment strategies, and containerization.

In this phase of the bootcamp students will get familiar with what it means to have machine learning products in production working reliably over time. In the previous phase, they learned how to deploy models; now they will learn how to monitor and maintain them.

By the end of this phase , students will be able to understand, build, evaluate AI systems using modern Large Language Model (LLM) technologies. They will develop foundational knowledge of LLM architectures, embeddings, vector search, and prompt engineering; gain hands-on experience constructing Retrieval-Augmented Generation (RAG) pipelines; perform fine-tuning and evaluation of small models; and design custom agentic systems using frameworks such as LangChain, pydanticAI, and MCP.

In this phase, students will learn to deploy and manage LLM applications. They will build FastAPI services, containerize them with Docker, and deploy AI systems to Google Cloud Run. Students will integrate monitoring and observability to ensure reliability, and they will learn to deploy complete RAG and agent pipelines end-to-end.

In the final phase of the bootcamp, students will take on a comprehensive capstone project that brings together everything they’ve learned. They’ll design, build, deploy, and monitor a complete machine learning system that solves a real-world problem. Working in teams, students will operate as a professional MLE group, using best practices from software engineering, data engineering, machine learning engineering, model monitoring, and LLM development. The bootcamp concludes with a presentation and live demo of their solution to instructors and peers.