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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Building and Deploying Generative AI Models
本课程是 Generative AI Fundamentals 专项课程 的一部分
位教师：Amreen Anbar
包含在 中
您将学到什么
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.
您将获得的技能
- Model Deployment
- Prompt Engineering
- Artificial Intelligence and Machine Learning (AI/ML)
- Development Environment
- Generative AI Agents
- LangChain
- Natural Language Processing
- Google Cloud Platform
- Embeddings
- PyTorch (Machine Learning Library)
- Model Evaluation
- Transfer Learning
- Generative Model Architectures
- Vector Databases
- Deep Learning
- Generative AI
- Large Language Modeling
- System Monitoring
要了解的详细信息
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最近已更新！
December 2025
作业
3 项作业
授课语言：英语（English）
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积累特定领域的专业知识
本课程是 Generative AI Fundamentals 专项课程 专项课程的一部分
在注册此课程时，您还会同时注册此专项课程。
- 向行业专家学习新概念
- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 获得可共享的职业证书
该课程共有3个模块
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.
涵盖的内容
18个视频11篇阅读材料1个作业
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.
涵盖的内容
13个视频2篇阅读材料1个作业
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.
涵盖的内容
15个视频1篇阅读材料1个作业
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