AI Engineer

AI Engineers build complex systems using foundation models, LLMs, and AI agents. You will learn how to design, build, and deploy AI systems. Includes **PyTorch**, **Streamlit**, **OpenAI**, **Hugging Face**, and more.

Syllabus

Welcome to the AI Engineer Career Path

Discover what you will learn on your journey to becoming an AI Engineer!

Neural Network Architectures

Learn neural network architectures with PyTorch to build deep learning models for image, text, and sequential data tasks.

Introduction to AI Transformers

Learn about what transformers are (the T of GPT) and how to work with them using Hugging Face libraries

Finetuning Transformer Models

Master the art of LLM finetuning with LoRA, QLoRA, and Hugging Face. Learn how to prepare, train and optimize models for specific tasks efficiently.

AI Engineer Portfolio Project: Intent Classification

Demonstrate your ability to build an end-to-end AI engineering project for an NLP text classification task by finetuning a transformer-based model with LoRA.

Intro to OpenAI API

Explore OpenAI’s API and learn how to write more effective generative AI prompts that help improve your results.

OpenAI API Coding with Python

Leverage the OpenAI API within your Python code. Learn to import OpenAI modules, use chat completion methods, and craft effective prompts.

Benchmarking LLMs

Learn how to integrate large language models using APIs, engineer effective prompts for reliable outputs, and evaluate LLM performance.

Build AI Applications with Streamlit

Learn Streamlit to build and deploy interactive AI applications with Python in this hands-on course.

Creating AI Applications using Retrieval-Augmented Generation (RAG)

Learn how to give your large language model the powers of retrieval with RAG, and build a RAG app with Streamlit and ChromaDB.

Best Practices in AI Deployment

Learn machine learning operations best practices to deploy, monitor, and maintain production AI systems that are reliable, secure, and cost-effective.