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
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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!
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