Journey to Become a Google Cloud Machine Learning Engineer

Journey to Become a Google Cloud Machine Learning Engineer

by Logan Song
Released September 2022
Publisher(s): Packt Publishing
ISBN: 9781803233727

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Book description

Prepare for the GCP ML certification exam along with exploring cloud computing and machine learning concepts and gaining Google Cloud ML skills

Key Features

  • A comprehensive yet easy-to-follow Google Cloud machine learning study guide
  • Explore full-spectrum and step-by-step practice examples to develop hands-on skills
  • Read through and learn from in-depth discussions of Google ML certification exam questions

Book Description

This book aims to provide a study guide to learn and master machine learning in Google Cloud: to build a broad and strong knowledge base, train hands-on skills, and get certified as a Google Cloud Machine Learning Engineer.

The book is for someone who has the basic Google Cloud Platform (GCP) knowledge and skills, and basic Python programming skills, and wants to learn machine learning in GCP to take their next step toward becoming a Google Cloud Certified Machine Learning professional.

The book starts by laying the foundations of Google Cloud Platform and Python programming, followed the by building blocks of machine learning, then focusing on machine learning in Google Cloud, and finally ends the studying for the Google Cloud Machine Learning certification by integrating all the knowledge and skills together.

The book is based on the graduate courses the author has been teaching at the University of Texas at Dallas. When going through the chapters, the reader is expected to study the concepts, complete the exercises, understand and practice the labs in the appendices, and study each exam question thoroughly. Then, at the end of the learning journey, you can expect to harvest the knowledge, skills, and a certificate.

What you will learn

  • Provision Google Cloud services related to data science and machine learning
  • Program with the Python programming language and data science libraries
  • Understand machine learning concepts and model development processes
  • Explore deep learning concepts and neural networks
  • Build, train, and deploy ML models with Google BigQuery ML, Keras, and Google Cloud Vertex AI
  • Discover the Google Cloud ML Application Programming Interface (API)
  • Prepare to achieve Google Cloud Professional Machine Learning Engineer certification

Who this book is for

Anyone from the cloud computing, data analytics, and machine learning domains, such as cloud engineers, data scientists, data engineers, ML practitioners, and engineers, will be able to acquire the knowledge and skills and achieve the Google Cloud professional ML Engineer certification with this study guide. Basic knowledge of Google Cloud Platform and Python programming is required to get the most out of this book.

Table of contents

  1. Journey to Become a Google Cloud Machine Learning Engineer
  2. Contributors
  3. About the author
  4. About the reviewer
  5. Preface
    1. Who this book is for
    2. What this book covers
    3. To get the most out of this book
    4. Download the example code files
    5. Download the color images
    6. Conventions used
    7. Get in touch
    8. Share Your Thoughts
  6. Part 1: Starting with GCP and Python
  7. Chapter 1: Comprehending Google Cloud Services
    1. Understanding the GCP global infrastructure
    2. Getting started with GCP
      1. Creating a free-tier GCP account
      2. Provisioning our first computer in Google Cloud
      3. Provisioning our first storage in Google Cloud
      4. Managing resources using GCP Cloud Shell
      5. GCP networking – virtual private clouds
    3. GCP organization structure
      1. The GCP resource hierarchy
      2. GCP projects
    4. GCP Identity and Access Management
      1. Authentication
      2. Authorization
      3. Auditing or accounting
      4. Service account
    5. GCP compute services
      1. GCE virtual machines
      2. Load balancers and managed instance groups
      3. Containers and Google Kubernetes Engine
      4. GCP Cloud Run
      5. GCP Cloud Functions
    6. GCP storage and database service spectrum
      1. GCP storage
      2. Google Cloud SQL
      3. Google Cloud Spanner
      4. Cloud Firestore
      5. Google Cloud Bigtable
    7. GCP big data and analytics services
      1. Google Cloud Dataproc
      2. Google Cloud Dataflow
      3. Google Cloud BigQuery
      4. Google Cloud Pub/Sub
    8. GCP artificial intelligence services
      1. Google Vertex AI
      2. Google Cloud ML APIs
    9. Summary
    10. Further reading
  8. Chapter 2: Mastering Python Programming
    1. Technical requirements
    2. The basics of Python
      1. Basic Python variables and operations
      2. Basic Python data structure
      3. Python conditions and loops
      4. Python functions
      5. Opening and closing files in Python
      6. An interesting problem
    3. Python data libraries and packages
      1. NumPy
      2. Pandas
      3. Matplotlib
      4. Seaborn
    4. Summary
    5. Further reading
  9. Part 2: Introducing Machine Learning
  10. Chapter 3: Preparing for ML Development
    1. Starting from business requirements
    2. Defining ML problems
      1. Is ML the best solution?
      2. ML problem categories
      3. ML model inputs and outputs
    3. Measuring ML solutions and data readiness
      1. ML model performance measurement
      2. Data readiness
    4. Collecting data
    5. Data engineering
      1. Data sampling and balancing
      2. Numerical value transformation
      3. Categorical value transformation
      4. Missing value handling
      5. Outlier processing
    6. Feature engineering
      1. Feature selection
      2. Feature synthesis
    7. Summary
    8. Further reading
  11. Chapter 4: Developing and Deploying ML Models
    1. Splitting the dataset
    2. Preparing the platform
    3. Training the model
      1. Linear regression
      2. Binary classification
      3. Support vector machine
      4. Decision tree and random forest
    4. Validating the model
      1. Model validation
      2. Confusion matrix
      3. ROC curve and AUC
      4. More classification metrics
    5. Tuning the model
      1. Overfitting and underfitting
      2. Regularization
      3. Hyperparameter tuning
    6. Testing and deploying the model
    7. Practicing model development with scikit-learn
    8. Summary
    9. Further reading
  12. 5
  13. Understanding Neural Networks and Deep Learning
    1. Neural networks and DL
    2. The cost function
    3. The optimizer algorithm
    4. The activation functions
    5. Convolutional Neural Networks
      1. The convolutional layer
      2. The pooling layer
      3. The fully connected layer
    6. Recurrent Neural Networks
    7. Long Short-Term Memory Networks
    8. Generative Adversarial networks
    9. Summary
    10. Further reading
  14. Part 3: Mastering ML in GCP
  15. 6
  16. Learning BQ/BQML, TensorFlow, and Keras
    1. GCP BQ
    2. GCP BQML
    3. Introduction to TensorFlow
      1. Understanding the concept of tensors
      2. How tensors flow
    4. Introduction to Keras
    5. Summary
    6. Further reading
  17. Chapter 7: Exploring Google Cloud Vertex AI
    1. Vertex AI data labeling and datasets
    2. Vertex AI Feature Store
    3. Vertex AI Workbench and notebooks
    4. Vertex AI Training
      1. Vertex AI AutoML
      2. The Vertex AI platform
    5. Vertex AI Models and Predictions
      1. Vertex AI endpoint prediction
      2. Vertex AI batch prediction
    6. Vertex AI Pipelines
    7. Vertex AI Metadata
    8. Vertex AI experiments and TensorBoard
    9. Summary
    10. Further reading
  18. Chapter 8: Discovering Google Cloud ML API
    1. Google Cloud Sight API
      1. The Cloud Vision API
      2. The Cloud Video API
    2. The Google Cloud Language API
    3. The Google Cloud Conversation API
    4. Summary
    5. Further reading
  19. Chapter 9: Using Google Cloud ML Best Practices
    1. ML environment setup
    2. ML data storage and processing
    3. ML model training
    4. ML model deployment
    5. ML workflow orchestration
    6. ML model continuous monitoring
    7. Summary
    8. Further reading
  20. Part 4: Accomplishing GCP ML Certification
  21. Chapter 10: Achieving the GCP ML Certification
    1. GCP ML exam practice questions
    2. Summary
  22. Part 5: Appendices
  23. Appendix 1: Practicing with Basic GCP Services
    1. Practicing using GCP services with the Cloud console
      1. Creating network VPCs using the GCP console
      2. Creating a public VM, vm1, within vpc1/subnet1 using the GCP console
      3. Creating a private VM, vm2, within vpc1/subnet2 using the GCP console
      4. Creating a private VM, vm8, within vpc2/subnet8 using the GCP console
      5. Creating peering between vpc1 and vpc2 using the GCP console
      6. Creating a GCS bucket from the GCP console
    2. Provisioning GCP resources using Google Cloud Shell
    3. Summary
  24. Appendix 2: Practicing Using the Python Data Libraries
    1. NumPy
      1. Generating NumPy arrays
      2. Operating NumPy arrays
    2. Pandas
      1. Series
      2. DataFrames
      3. Missing data handling
      4. GroupBy
      5. Operations
    3. Matplotlib
    4. Seaborn
    5. Summary
  25. Appendix 3: Practicing with Scikit-Learn
    1. Data preparation
    2. Regression
      1. Simple linear regression
      2. Multiple linear regression
      3. Polynomial/non-linear regression
    3. Classification
    4. Summary
  26. Appendix 4: Practicing with Google Vertex AI
    1. Vertex AI – enabling its API
    2. Vertex AI – datasets
    3. Vertex AI – labeling tasks
    4. Vertex AI – training
    5. Vertex AI – predictions (Vertex AI Endpoint)
      1. Deploying the model via Models
      2. Deploying the model via Endpoints
    6. Vertex AI – predictions (Batch Prediction)
    7. Vertex AI – Workbench
    8. Vertex AI – Feature Store
    9. Vertex AI – pipelines and metadata
    10. Vertex AI – model monitoring
    11. Summary
  27. Appendix 5: Practicing with Google Cloud ML API
    1. Google Cloud Vision API
    2. Google Cloud NLP API
    3. Google Cloud Speech-to-Text API
    4. Google Cloud Text-To-Speech API
    5. Google Cloud Translation API
    6. Google Cloud Dialogflow API
    7. Summary
  28. Index
    1. Why subscribe?
  29. Other Books You May Enjoy
    1. Packt is searching for authors like you
    2. Share Your Thoughts

Product information

  • Title: Journey to Become a Google Cloud Machine Learning Engineer
  • Author(s): Logan Song
  • Release date: September 2022
  • Publisher(s): Packt Publishing
  • ISBN: 9781803233727