The Definitive Guide to Google Vertex AI: Accelerate your machine learning journey with Google Cloud Vertex AI and MLOps 9781801815260

Table of contents :
The Definitive Guide to Google Vertex AI
Contributors
About the authors
About the reviewers
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
Share Your Thoughts
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Part 1:The Importance of MLOps in a Real-World ML Deployment
1
Machine Learning Project Life Cycle and Challenges
ML project life cycle
Common challenges in developing real-world ML solutions
Data collection and security
Non-representative training data
Poor quality of data
Underfitting the training dataset
Overfitting the training dataset
Infrastructure requirements
Limitations of ML
Data-related concerns
Deterministic nature of problems
Lack of interpretability and reproducibility
Concerns related to cost and customizations
Ethical concerns and bias
Summary
2
What Is MLOps, and Why Is It So Important for Every ML Team?
Why is MLOps important?
Implementing different MLOps maturity levels
MLOps maturity level 0
MLOps maturity level 1 – automating basic ML steps
MLOps maturity level 2 – automated model deployments
How can Vertex AI help with implementing MLOps?
Summary
Part 2: Machine Learning Tools for Custom Models on Google Cloud
3
It’s All About Data – Options to Store and Transform ML Datasets
Moving data to Google Cloud
Google Cloud Storage Transfer tools
BigQuery Data Transfer Service
Storage Transfer Service
Transfer Appliance
Where to store data
GCS – object storage
BQ – data warehouse
Transforming data
Ad hoc transformations within Jupyter Notebook
Cloud Data Fusion
Dataflow pipelines for scalable data transformations
Summary
4
Vertex AI Workbench – a One-Stop Tool for AI/ML Development Needs
What is Jupyter Notebook?
Getting started with Jupyter Notebook
Vertex AI Workbench
Getting started with Vertex AI Workbench
Custom containers for Vertex AI Workbench
Scheduling notebooks in Vertex AI
Configuring notebook executions
Summary
5
No-Code Options for Building ML Models
ML modeling options in Google Cloud
What is AutoML?
Vertex AI AutoML
How to create a Vertex AI AutoML model using tabular data
Importing data to use with Vertex AI AutoML
Training the AutoML model for tabular/structured data
Generating predictions using the recently trained model
Deploying a model in Vertex AI
Generating predictions
Generating predictions programmatically
Summary
6
Low-Code Options for Building ML Models
What is BQML?
Getting started with BigQuery
Using BQML for feature transformations
Manual preprocessing
Building ML models with BQML
Creating BQML models
Hyperparameter tuning with BQML
Evaluating trained models
Doing inference with BQML
User exercise
Summary
7
Training Fully Custom ML Models with Vertex AI
Technical requirements
Building a basic deep learning model with TensorFlow
Experiment – converting black-and-white images into color images
Packaging a model to submit it to Vertex AI as a training job
Monitoring model training progress
Evaluating trained models
Summary
8
ML Model Explainability
What is Explainable AI and why is it important for MLOps practitioners?
Building trust and confidence
Explainable AI techniques
Global versus local explainability
Techniques for image data
Techniques for tabular data
Techniques for text data
Explainable AI features available in Google Cloud Vertex AI
Feature-based explanation techniques available on Vertex AI
Using the model feature importance (SHAP-based) capability with AutoML for tabular data
Exercise 1
Exercise 2
Example-based explanations
Key steps to use example-based explanations
Exercise 3
Summary
References
9
Model Optimizations – Hyperparameter Tuning and NAS
Technical requirements
What is HPT and why is it important?
What are hyperparameters?
Why HPT?
Search algorithms
Setting up HPT jobs on Vertex AI
What is NAS and how is it different from HPT?
Search space
Optimization method
Evaluation method
NAS on Vertex AI overview
NAS best practices
Summary
10
Vertex AI Deployment and Automation Tools – Orchestration through Managed Kubeflow Pipelines
Technical requirements
Orchestrating ML workflows using Vertex AI Pipelines (managed Kubeflow pipelines)
Developing Vertex AI Pipeline using Python
Pipeline components
Orchestrating ML workflows using Cloud Composer (managed Airflow)
Creating a Cloud Composer environment
Vertex AI Pipelines versus Cloud Composer
Getting predictions on Vertex AI
Getting online predictions
Getting batch predictions
Managing deployed models on Vertex AI
Multiple models – single endpoint
Single model – multiple endpoints
Compute resources and scaling
Summary
11
MLOps Governance with Vertex AI
What is MLOps governance and what are its key components?
Data governance
Model governance
Enterprise scenarios that highlight the importance of MLOps governance
Scenario 1 – limiting bias in AI solutions
Scenario 2 – the need to constantly monitor shifts in feature distributions
Scenario 3 – the need to monitor costs
Scenario 4 – monitoring how the training data is sourced
Tools in Vertex AI that can help with governance
Model Registry
Metadata Store
Feature Store
Vertex AI pipelines
Model Monitoring
Billing monitoring
Summary
References
Part 3: Prebuilt/Turnkey ML Solutions Available in GCP
12
Vertex AI – Generative AI Tools
GenAI fundamentals
GenAI versus traditional AI
Types of GenAI models
Challenges of GenAI
LLM evaluation
GenAI with Vertex AI
Understanding foundation models
What is a prompt?
Using Vertex AI GenAI models through GenAI Studio
Example 1 – using GenAI Studio language models to generate text
Example 2 – submitting examples along with the text prompt in structured format to get generated output in a specific format
Example 3 – generating images using GenAI Studio (Vision)
Example 4 – generating code samples
Building and deploying GenAI applications with Vertex AI
Enhancing GenAI performance with model tuning in Vertex AI
Using Vertex AI supervised tuning
Safety filters for generated content
Summary
References
13
Document AI – An End-to-End Solution for Processing Documents
Technical requirements
What is Document AI?
Document AI processors
Overview of existing Document AI processors
Using Document AI processors
Creating custom Document AI processors
Summary
14
ML APIs for Vision, NLP, and Speech
Vision AI on Google Cloud
Vision AI
Video AI
Translation AI on Google Cloud
Cloud Translation API
AutoML Translation
Translation Hub
Natural Language AI on Google Cloud
AutoML for Text Analysis
Natural Language API
Healthcare Natural Language API
Speech AI on Google Cloud
Speech-to-Text
Text-to-Speech
Summary
Part 4: Building Real-World ML Solutions with Google Cloud
15
Recommender Systems – Predict What Movies a User Would Like to Watch
Different types of recommender systems
Real-world evaluation of recommender systems
Deploying a movie recommender system on Vertex AI
Data preparation
Model building
Local model testing
Deploying the model on Google Cloud
Using the model for inference
Summary
References
16
Vision-Based Defect Detection System – Machines Can See Now!
Technical requirements
Vision-based defect detection
Dataset
Importing useful libraries
Loading and verifying data
Checking few samples
Data preparation
Splitting data into train and test
Final preparation of training and testing data
TF model architecture
Compiling the model
Training the model
Plotting the training progress
Results
Deploying a vision model to a Vertex AI endpoint
Saving model to Google Cloud Storage (GCS)
Uploading the TF model to the Vertex Model Registry
Creating a Vertex AI endpoint
Deploying a model to the Vertex AI endpoint
Getting online predictions from a vision model
Summary
17
Natural Language Models – Detecting Fake News Articles!
Technical requirements
Detecting fake news using NLP
Fake news classification with random forest
About the dataset
Importing useful libraries
Reading and verifying the data
NULL value check
Combining title and text into a single column
Cleaning and pre-processing data
Separating the data and labels
Converting text into numeric data
Splitting the data
Defining the random forest classifier
Training the model
Predicting the test data
Checking the results/metrics on the test dataset
Confusion matrix
Launching model training on Vertex AI
Setting configurations
Initializing the Vertex AI SDK
Defining the Vertex AI training job
Running the Vertex AI job
BERT-based fake news classification
BERT for fake news classification
Importing useful libraries
The dataset
Data preparation
Splitting the data
Creating data loader objects for batching
Loading the pre-trained BERT model
Scheduler
Training BERT
Loading model weights for evaluation
Calculating the accuracy of the test dataset
Classification report
Summary
Index
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