Becoming an AI Engineer with LLM Application Development

Becoming an AI Engineer with LLM Application Development, available at $54.99, with 32 lectures, 5 quizzes, and has 5 subscribers.

You will learn about 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 This course is ideal for individuals who are Professional software developers who are new to generative AI application development or Computer science students who are interested in generative AI application development or Web developers who is seeking to build an generative AI as a side project or Python developers who is seeking to build an generative AI as a side project It is particularly useful for Professional software developers who are new to generative AI application development or Computer science students who are interested in generative AI application development or Web developers who is seeking to build an generative AI as a side project or Python developers who is seeking to build an generative AI as a side project.

Enroll now: Becoming an AI Engineer with LLM Application Development

Summary

Title: Becoming an AI Engineer with LLM Application Development

Price: $54.99

Number of Lectures: 32

Number of Quizzes: 5

Number of Published Lectures: 32

Number of Published Quizzes: 5

Number of Curriculum Items: 37

Number of Published Curriculum Objects: 37

Number of Practice Tests: 2

Number of Published Practice Tests: 2

Original Price: $54.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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 Exam

What 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 videos

2. Supplement learning resources to keep up to date with the latest trend

3. Open-source codebase to help you kick-start your AI engineer journey

4. Various online quizzes to help you familiarize yourself with the contents and the skills

5. Q&A with the instructor

6. Programming test with hands-on online practice

Who 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.

Course Curriculum

Chapter 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

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

Lecture 1: Python Environment Set-up

Lecture 2: Visual Studio Code Set-up

Lecture 3: GitHub Tour

Lecture 4: Hugging Face Tour

Lecture 5: OpenAI API Platform Tour

Lecture 6: Supplement Materials

Chapter 3: Chapter 3 – Develop your API for your generative AI applications

Lecture 1: What are HTTP and REST API?

Lecture 2: API development and deployment workflow

Lecture 3: Architecting your API backend application with FastAPI

Lecture 4: Demonstration – Architecting your FastAPI Applications

Lecture 5: Python API development with FastAPI

Lecture 6: Containerize your API application with Docker

Lecture 7: Deploying your API with Hugging Face Space

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

Lecture 1: Introduction to Gradio and develop your first application

Lecture 2: Demonstration: Deploy your first Gradio application

Chapter 5: Chapter 5 – Streamline API Delivery with Automated Test Pipeline

Lecture 1: What is DevOps?

Lecture 2: What is software testing?

Lecture 3: Introduction to Pytest and Software Testing Frameworks

Lecture 4: Demonstration – Pytest for Generative AI Applications

Lecture 5: Demonstration – Automated Test Workflow with GitHub Actions

Chapter 6: Chapter 6 – Course Summary / Advanced Topics

Lecture 1: What is domain-adaptation?

Lecture 2: What is retrieval-augmented generation (RAG)?

Lecture 3: Course Recap

Lecture 4: Future Learning

Instructors

Mark Chen
Research Engineer / Full-stack Developer 研究工程師/全端開發人員

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Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!