Data Science on the Google Cloud Platform. 2nd Edition Valliappa Lakshmanan

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• Preface
  • Who This Book Is For
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Making Better Decisions Based on Data
  • Many Similar Decisions
  • The Role of Data Scientists
    • Scrappy Environment
    • Full Stack Cloud Data Scientists
    • Collaboration
  • Best Practices
    • Simple to Complex Solutions
    • Cloud Computing
    • Serverless
  • A Probabilistic Decision
    • Probabilistic Approach
    • Probability Density Function
    • Cumulative Distribution Function
  • Choices Made
    • Choosing Cloud
    • Not a Reference Book
    • Getting Started with the Code
  • Agile Architecture for Data Science on Google Cloud
    • What Is Agile Architecture?
    • No-Code, Low-Code
    • Use Managed Services
  • Summary
  • Suggested Resources
• 2. Ingesting Data into the Cloud
  • Airline On-Time Performance Data
    • Knowability
    • Causality
    • TrainingServing Skew
    • Downloading Data
    • Hub-and-Spoke Architecture
    • Dataset Fields
  • Separation of Compute and Storage
    • Scaling Up
    • Scaling Out with Sharded Data
    • Scaling Out with Data-in-Place
  • Ingesting Data
    • Reverse Engineering a Web Form
    • Dataset Download
    • Exploration and Cleanup
    • Uploading Data to Google Cloud Storage
  • Loading Data into Google BigQuery
    • Advantages of a Serverless Columnar Database
    • Staging on Cloud Storage
    • Access Control
    • Ingesting CSV Files
    • Partitioning
  • Scheduling Monthly Downloads
    • Ingesting in Python
    • Cloud Run
    • Securing Cloud Run
    • Deploying and Invoking Cloud Run
    • Scheduling Cloud Run
  • Summary
  • Code Break
  • Suggested Resources
• 3. Creating Compelling Dashboards
  • Explain Your Model with Dashboards
    • Why Build a Dashboard First?
    • Accuracy, Honesty, and Good Design
  • Loading Data into Cloud SQL
    • Create a Google Cloud SQL Instance
    • Create Table of Data
    • Interacting with the Database
  • Querying Using BigQuery
    • Schema Exploration
    • Using Preview
    • Using Table Explorer
    • Creating BigQuery View
  • Building Our First Model
    • Contingency Table
    • Threshold Optimization
  • Building a Dashboard
    • Getting Started with Data Studio
    • Creating Charts
    • Adding End-User Controls
    • Showing Proportions with a Pie Chart
    • Explaining a Contingency Table
  • Modern Business Intelligence
    • Digitization
    • Natural Language Queries
    • Connected Sheets
  • Summary
  • Suggested Resources
• 4. Streaming Data: Publication and Ingest with Pub/Sub and Dataflow
  • Designing the Event Feed
    • Transformations Needed
    • Architecture
    • Getting Airport Information
    • Sharing Data
      • Sharing a Cloud Storage dataset
      • Sharing a BigQuery dataset
      • Dataplex and Analytics Hub
  • Time Correction
    • Apache Beam/Cloud Dataflow
    • Parsing Airports Data
    • Adding Time Zone Information
    • Converting Times to UTC
    • Correcting Dates
    • Creating Events
    • Reading and Writing to the Cloud
    • Running the Pipeline in the Cloud
  • Publishing an Event Stream to Cloud Pub/Sub
    • Speed-Up Factor
    • Get Records to Publish
    • How Many Topics?
    • Iterating Through Records
    • Building a Batch of Events
    • Publishing a Batch of Events
  • Real-Time Stream Processing
    • Streaming in Dataflow
    • Windowing a Pipeline
    • Streaming Aggregation
    • Using Event Timestamps
    • Executing the Stream Processing
    • Analyzing Streaming Data in BigQuery
  • Real-Time Dashboard
  • Summary
  • Suggested Resources
• 5. Interactive Data Exploration with Vertex AI Workbench
  • Exploratory Data Analysis
    • Exploration with SQL
    • Reading a Query Explanation
  • Exploratory Data Analysis in Vertex AI Workbench
    • Jupyter Notebooks
    • Creating a Notebook
    • Jupyter Commands
    • Installing Packages
    • Jupyter Magic for Google Cloud
  • Exploring Arrival Delays
    • Basic Statistics
    • Plotting Distributions
    • Quality Control
      • Oddball values
      • Outlier removal: Big data is different
      • Filtering data on occurrence frequency
    • Arrival Delay Conditioned on Departure Delay
      • Distribution of arrival delays
      • Applying a probabilistic decision threshold
      • Empirical probability distribution function
      • The answer is...
  • Evaluating the Model
    • Random Shuffling
    • Splitting by Date
    • Training and Testing
  • Summary
  • Suggested Resources
• 6. Bayesian Classifier with Apache Spark on Cloud Dataproc
  • MapReduce and the Hadoop Ecosystem
    • How MapReduce Works
    • Apache Hadoop
  • Google Cloud Dataproc
    • Need for Higher-Level Tools
    • Jobs, Not Clusters
    • Preinstalling Software
  • Quantization Using Spark SQL
    • JupyterLab on Cloud Dataproc
    • Independence Check Using BigQuery
    • Spark SQL in JupyterLab
    • Histogram Equalization
  • Bayesian Classification
    • Bayes in Each Bin
    • Evaluating the Model
    • Dynamically Resizing Clusters
    • Comparing to Single Threshold Model
  • Orchestration
    • Submitting a Spark Job
    • Workflow Template
    • Cloud Composer
    • Autoscaling
    • Serverless Spark
  • Summary
  • Suggested Resources
• 7. Logistic Regression Using Spark ML
  • Logistic Regression
    • How Logistic Regression Works
    • Spark ML Library
    • Getting Started with Spark Machine Learning
  • Spark Logistic Regression
    • Creating a Training Dataset
      • Dealing with corner cases
      • Creating training examples
    • Training the Model
    • Predicting Using the Model
    • Evaluating a Model
  • Feature Engineering
    • Experimental Framework
      • Choosing a metric
      • Creating the held-out dataset
    • Feature Selection
      • Creating a large cluster
      • Increasing quota
      • Autoscale up and down
      • Removing features
    • Feature Transformations
      • Scaling
      • Clipping
    • Feature Creation
    • Categorical Variables
    • Repeatable, Real Time
  • Summary
  • Suggested Resources
• 8. Machine Learning with BigQuery ML
  • Logistic Regression
    • Presplit Data
    • Interrogating the Model
    • Evaluating the Model
    • Scale and Simplicity
  • Nonlinear Machine Learning
    • XGBoost
    • Hyperparameter Tuning
    • Vertex AI AutoML Tables
  • Time Window Features
    • Taxi-Out Time
    • Compounding Delays
    • Causality
  • Time Features
    • Departure Hour
    • Transform Clause
    • Categorical Variable
    • Feature Cross
  • Summary
  • Suggested Resources
• 9. Machine Learning with TensorFlow in Vertex AI
  • Toward More Complex Models
    • Preparing BigQuery Data for TensorFlow
    • Reading Data into TensorFlow
  • Training and Evaluation in Keras
    • Model Function
    • Features
    • Inputs
    • Training the Keras Model
    • Saving and Exporting
    • Deep Neural Network
  • Wide-and-Deep Model in Keras
    • Representing Air Traffic Corridors
    • Bucketing
    • Feature Crossing
    • Wide-and-Deep Classifier
  • Deploying a Trained TensorFlow Model to Vertex AI
    • Concepts
    • Uploading Model
    • Creating Endpoint
    • Deploying Model to Endpoint
    • Invoking the Deployed Model
  • Summary
  • Suggested Resources
• 10. Getting Ready for MLOps with Vertex AI
  • Developing and Deploying Using Python
    • Writing model.py
    • Writing the Training Pipeline
    • Predefined Split
    • AutoML
  • Hyperparameter Tuning
    • Parameterize Model
    • Shorten Training Run
    • Metrics During Training
    • Hyperparameter Tuning Pipeline
    • Best Trial to Completion
  • Explaining the Model
    • Configuring Explanations Metadata
    • Creating and Deploying Model
    • Obtaining Explanations
  • Summary
  • Suggested Resources
• 11. Time-Windowed Features for Real-Time Machine Learning
  • Time Averages
    • Apache Beam and Cloud Dataflow
      • Why Apache Beam?
      • Why Dataflow?
      • Starting points
    • Reading and Writing
      • Reading from BigQuery
      • Local JSON input
      • Filtering
    • Time Windowing
      • Assigning a timestamp
      • Sliding windows
      • Computing moving average
      • Removing duplicates
  • Machine Learning Training
    • Machine Learning Dataset
      • Label
      • Data split
      • Distance bug
      • Monitoring and verification
    • Training the Model
      • Changes from Chapter 10
      • AutoML model
      • Custom model
  • Streaming Predictions
    • Reuse Transforms
    • Input and Output
    • Invoking Model
    • Reusing Endpoint
      • Shared handle
      • Per-worker instance
    • Batching Predictions
  • Streaming Pipeline
    • Writing to BigQuery
    • Executing Streaming Pipeline
    • Late and Out-of-Order Records
      • Uniformly distributed delay
      • Exponential distribution
      • Normal distribution
      • Watermarks and triggers
    • Possible Streaming Sinks
      • Choosing a sink
      • Cloud Bigtable
        • Designing tables
        • Designing the row key
        • Streaming into Cloud Bigtable
        • Querying from Cloud Bigtable
  • Summary
  • Suggested Resources
• 12. The Full Dataset
  • Four Years of Data
    • Creating Dataset
      • Dataset split
      • Shuffling data
      • Need for continuous training
      • More powerful machines
    • Training Model
    • Evaluation
      • RMSE
      • Confusion matrix
      • Impact of threshold
      • Impact of a feature
      • Analyzing errors
      • Categorical features
  • Summary
  • Suggested Resources
• Conclusion
• A. Considerations for Sensitive Data Within Machine Learning Datasets
  • Handling Sensitive Information
    • Sensitive Data in Columns
    • Sensitive Data in Natural Language Datasets
    • Sensitive Data in Free-Form Unstructured Data
    • Sensitive Data in a Combination of Fields
    • Sensitive Data in Unstructured Content
  • Protecting Sensitive Data
    • Removing Sensitive Data
    • Masking Sensitive Data
    • Coarsening Sensitive Data
  • Establishing a Governance Policy
• Index

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