This is the code repository for Google Machine Learning and Generative AI for Solutions Architects, published by Packt.
Build efficient and scalable AI/ML solutions on Google Cloud
Whether you are relatively new to AI/ML or an experienced solutions architect working to address emerging business challenges, this book covers everything you need to design, implement, and manage complex AI/ML workloads on Google Cloud.
This book covers the following exciting features:
- Build solutions with open-source offerings on Google Cloud, such as TensorFlow, PyTorch, and Spark
- Source, understand, and prepare data for ML workloads
- Build, train, and deploy ML models on Google Cloud
- Create an effective MLOps strategy and implement MLOps workloads on Google Cloud
- Discover common challenges in typical AI/ML projects and get solutions from experts
- Explore vector databases and their importance in Generative AI applications
- Uncover new Gen AI patterns such as Retrieval Augmented Generation (RAG), agents, and agentic workflows
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
import matplotlib.pyplot as plt
import pandas as pd
# Load dataset
data = load_wine()
df = pd.DataFrame(data.data, columns=data.feature_names)Following is what you need for this book: This book is for aspiring solutions architects looking to design and implement AI/ML solutions on Google Cloud. Although this book is suitable for both beginners and experienced practitioners, basic knowledge of Python and ML concepts is required. The book focuses on how AI/ML is used in the real world on Google Cloud. It briefly covers the basics at the beginning to establish a baseline for you, but it does not go into depth on the underlying mathematical concepts that are readily available in academic material.
With the following software and hardware list you can run all code files present in the book (Chapter 1-18).
| Chapter | Software required | OS required |
|---|---|---|
| 1-18 | Python | Linux |
| 1-18 | Scikit-learn | Linux |
| 1-18 | TensorFlow | Linux |
Kieran Kavanagh is a Principal Architect at Google Cloud, working with Google’s largest retail customers and driving some of the industry's most challenging digital transformation and generative AI initiatives. Before joining Google, he was a Principal AI/ML Solutions Architect in Strategic Accounts at Amazon Web Services (AWS), building some of the most complex AI/ML systems in the world. He was also a Principal Architect at AT&T, leading their Mobile Internet infrastructure design, and he is a public speaker on the topics of AI/ML, MLOps, and large-scale cloud transformation. Originally from Cork, Ireland, he now lives in Atlanta, GA, with his wife, Katelyn.
This repository provides support materials and examples for the Google Machine Learning and Generative AI for Solutions Architects book.
Each of the code examples in this repository are intended to accompany that book. Each top-level folder in this repository is associated with a chapter in the book. The chapter contents explain the concepts being represented in the notebooks. In many cases, the chapter contents outline prerequisite activities that need to be performed before running the code in the notebooks. Please ensure that you have read the relevant chapter sections before executing the code in these notebooks.
There are also some generic prerequisite steps outlined here.