Transition from theoretical concepts to production-ready engineering in this hands-on course which is the final part in "Fundamentals of Generative AI" specialization. Designed for learners ready to move beyond the theory, this course focuses entirely on construction: you won't just learn about Large Language Models (LLMs); you will build, refine, and deploy them.
Building and Deploying Generative AI Models
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Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Construct and evaluate Transformer-based LLMs from scratch using PyTorch and industry metrics like ROUGE and BLEU.
Engineer Retrieval Augmented Generation (RAG) pipelines using LangChain to integrate current, domain-specific knowledge into models.
Deploy autonomous AI Agents to production environments on Google Cloud Platform (Vertex AI) using professional workflows.
Skills you'll gain
- PyTorch (Machine Learning Library)
- System Monitoring
- Model Deployment
- Generative AI Agents
- Artificial Intelligence and Machine Learning (AI/ML)
- Generative Model Architectures
- Google Cloud Platform
- Vector Databases
- Model Evaluation
- Transfer Learning
- Generative AI
- Development Environment
- Embeddings
- Natural Language Processing
- Deep Learning
- Prompt Engineering
- Large Language Modeling
- LangChain
Details to know
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Recently updated!
December 2025
Assessments
3 assignments
Taught in English
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There are 3 modules in this course
In this module, we dive deep into the Transformer architecture, its core mechanics, and different transformer architecture types (encoder-only, decoder-only, encoder-decoder). We gain hands-on experience by building and training a complete suite of PyTorch-based models from scratch. The module concludes with strategic deployment skills, teaching when to build custom models versus leveraging pre-trained models for efficiency and state-of-the-art results.
What's included
18 videos11 readings1 assignment
Module 2 addresses the limitations of static knowledge and hallucinations in Large Language Models (LLMs) by introducing Retrieval Augmented Generation (RAG). Learners will progress from building fundamental pipelines with Ollama and LangChain to implementing production-ready systems by adding rigorous RAG evaluation and utilizing advanced techniques such as custom chunking strategies, vector stores, reranking, and query transformations to optimize context retrieval and response generation. The module concludes with an overview of another adaptation technique called finetuning and a comparison of RAG vs. finetuning.
What's included
13 videos2 readings1 assignment
Module 3 marks a pivotal transition from passive information retrieval to the dynamic realm of autonomous AI Agents, anchored by the "Understand, Think, Take Action" conceptual framework. Students will critically evaluate development ecosystems before applying these concepts to build a functional Summarizer Agent. The module emphasizes professional engineering standards, guiding learners through a complete lifecycle that includes environment management with Poetry, deployment to the Vertex AI Engine, and the implementation of robust performance monitoring using Google Cloud Platform’s logging and tracing tools.
What's included
15 videos1 reading1 assignment
Offered by
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