Generative AI Engineering: Design, Development, and Applications
Course provided by Model Institute of Engineering & Technology
5 modules
Explore the fundamentals of Artificial Intelligence & Machine Learning
4-4.5 Level NCrF
National Credit Framework
320 Hours
Flexible Learning
Beginner Level
No prior experience required
Certificate Diploma Course
9-10 Credits
Course Overview
This program, offered by IBM through Coursera and enhanced with MIET’s hands-on training, blends world-class AI curriculum with region-specific case studies. Students gain practical experience in MIET’s AI Lab using tools like PyTorch, LangChain, and Retrieval-Augmented Generation (RAG) to solve real-world problems. The course includes skill-focused workshops on prompt engineering, model fine-tuning, and deploying advanced transformer models such as BERT and GPT.
Key Learning Highlights
- Offered by IBM through Coursera
Blended IBM Coursera curriculum with MIET’s hands-on classes and region-specific AI case studies. - Practical training in MIET’s AI lab using tools like PyTorch, LangChain, and RAG for real-world applications.
- Skill-focused workshops on prompt engineering, model fine-tuning, and transformer deployment (e.g., BERT, GPT).
Tools & Platform Used
Learning Outcome
By the end of this course, students will be able to:
Apply PyTorch, LangChain, and RAG to develop real-world AI solutions.
Design and implement effective prompts for diverse AI use cases.
Fine-tune and deploy transformer models such as BERT and GPT.
Integrate region-specific case studies into AI problem-solving approaches.
Build industry-ready skills for roles in AI engineering and machine learning.
Master the course with just 5 Modules
This course takes learners from the fundamentals of Generative AI and prompt construction to advanced tuning techniques. Beginning with the principles behind AI-generated text and images, participants progress to hands-on experimentation with real-world use cases. The journey ends with a deep dive into emerging trends, ethical implications, and future opportunities in the rapidly evolving field of GenAI.
Fundamentals of Generative AI
Learn the basics of AI and generative AI, including LLMs like GPT and BERT.
Understand prompt engineering, NLP concepts, and ethical considerations.
Explore real-world use cases across various industries.
AI Model Development and Fine-Tuning
Gain hands-on skills in Python, PyTorch, Keras, SciPy, and Scikit-learn.
Apply parameter-efficient fine-tuning methods such as LoRA and QLoRA.
Master tokenization, embedding techniques, and Hugging Face Transformers.
Building Generative AI Applications
Develop AI agents and chatbots using Flask, LangChain, and RAG.
Deploy NLP-based applications and integrate with IBM Watson and Hugging Face.
Work through case studies to solve practical industry problems.
Advanced NLP and Autonomous Systems
Dive into advanced transformer architectures and reinforcement learning.
Build AI-driven Q&A systems and explore swarm intelligence.
Implement secure communication protocols for AI systems.
Capstone Project
Design and develop a complete generative AI application.
Integrate LLMs, prompt engineering, and frameworks like LangChain or RAG.
Deliver an industry-relevant, end-to-end AI solution.
Roles
- Junior Machine Learning Developer
- AI Application Tester
- AI Product Developer
