Becoming An Ai Engineer With Llm Application Development

A concise guide for AI engineers to develop and deploy LLM-powered applications

What you'll learn

Learn the fundamental of LLM and generative AI

Learn the fundamental of API development

Learn the fundamental of Gradio framework

Develop your own AI chatbot in a day

Deploy your solution with Hugging Face Space

Automate your application development and deployment workflow to improve software quality and delivery speed

Requirements

Passion for AI

Computer (MacOS, Linux, or Windows)

Stable Network Connection

Python Installed - Recommended version: 3.9 or 3.10

Visual Studio Code Installed

Hugging Face Pro Subscription

OpenAI API Subscription

GitHub Account (Free or Pro)

Description

Becoming an AI Engineer with LLM Application Development| A concise guide for AI engineers to develop and deploy generative AI applications |What is generative AI? Why you should be a part of this revolution?Generative AI is a truly transformative technology that allows us to engineer and deploy various AI applications like chatbots and other automation workflows without costly upfront investments. Therefore, there is an emerging trend that many companies, even if not within the technology domains like finance and health care, are trying to adopt AI applications like ChatGPT. Here is what an AI engineer could do to help these organizations develop and deploy a valuable and cost-effective AI application using various open or closed-source models. If you want to be a part of this revolution, this course is right for you to learn the fundamental concepts and practical skills to become an AI engineer nowadays.What can I learn from this course?- Chapter 1 - Introduction to Generative AI- Chapter 2 - Environment Set-up / Generative AI Platform Tours- Chapter 3 - Develop your API endpoint for your generative AI applications- Chapter 4 - Develop and Deploy with your Front-end Interface- Chapter 5 - Streamline API Delivery with Automated Test and Deployment Pipeline- Chapter 6 - Course Summary / Final ExamWhat can I gain from this course?This course has a wide range of materials to help you become familiar with the concepts and skills to design, develop, and deploy an AI application; those resources include:1. On-demand lecture videos2. Supplement learning resources to keep up to date with the latest trend3. Open-source codebase to help you kick-start your AI engineer journey4. Various online quizzes to help you familiarize yourself with the contents and the skills5. Q&A with the instructor6. Programming test with hands-on online practiceWho is my instructor?Mark is an entrepreneur and computer science student at the University of London who lives in Taiwan. He founded Mindify AI, a company aimed at helping software engineers learn new codebases faster with its flagship product, Mindify Chat. Mark is also involved in AI and quantum AI research, working on innovative projects, including utility-scale quantum generative AI models for the Google Quantum Application XPRIZE. In addition to his business ventures, Mark creates Notion templates and Udemy courses, generating side income. Mark's recent achievements include developing algorithms, leading research projects, starting a new company, and gaining traction for Mindify AI. He is dedicated to making his products profitable and advancing his research and business efforts.

Overview

Section 1: Chapter 1 - Introduction to Generative AI

Lecture 1 Course Overview

Lecture 2 What is artificial intelligence (AI)?

Lecture 3 What is generative AI (GenAI)?

Lecture 4 What are large-language models (LLMs)?

Lecture 5 What is prompt engineering?

Lecture 6 What is application programming interface (API)?

Lecture 7 What is LangChain?

Lecture 8 Supplement Materials

Section 2: Chapter 2 - Environment Set-up / Generative AI Platform Tours

Lecture 9 Python Environment Set-up

Lecture 10 Visual Studio Code Set-up

Lecture 11 GitHub Tour

Lecture 12 Hugging Face Tour

Lecture 13 OpenAI API Platform Tour

Lecture 14 Supplement Materials

Section 3: Chapter 3 - Develop your API for your generative AI applications

Lecture 15 What are HTTP and REST API?

Lecture 16 API development and deployment workflow

Lecture 17 Architecting your API backend application with FastAPI

Lecture 18 Demonstration - Architecting your FastAPI Applications

Lecture 19 Python API development with FastAPI

Lecture 20 Containerize your API application with Docker

Lecture 21 Deploying your API with Hugging Face Space

Section 4: Chapter 4 - Develop and Deploy with your Front-end Interface

Lecture 22 Introduction to Gradio and develop your first application

Lecture 23 Demonstration: Deploy your first Gradio application

Section 5: Chapter 5 - Streamline API Delivery with Automated Test Pipeline

Lecture 24 What is DevOps?

Lecture 25 What is software testing?

Lecture 26 Introduction to Pytest and Software Testing Frameworks

Lecture 27 Demonstration - Pytest for Generative AI Applications

Lecture 28 Demonstration - Automated Test Workflow with GitHub Actions

Section 6: Chapter 6 - Course Summary / Advanced Topics

Lecture 29 What is domain-adaptation?

Lecture 30 What is retrieval-augmented generation (RAG)?

Lecture 31 Course Recap

Lecture 32 Future Learning

Professional software developers who are new to generative AI application development,Computer science students who are interested in generative AI application development,Web developers who is seeking to build an generative AI as a side project,Python developers who is seeking to build an generative AI as a side project