- 1. Cloud Functions
- 1.1. Creating a public HTTP Google Cloud Function
- 1.2. Creating a Secure HTTP Google Cloud Function
- 1.3. Accessing Environment Variables at runtime
- 1.4. Sending Emails from Cloud Functions with SendGrid
- 1.5. Deploying Cloud Functions with a GitLab CI/CD Pipeline
- 1.6. Responding to SMS Messages with Twilio and Cloud Functions
- 1.7. Unit Testing with GitLab and Cloud Functions
- 1.8. Building an API Gateway to Gather Telemetry Data
- 2. Google Cloud Run
- 2.1. Deploying a Prebuilt Hello World Container
- 2.2. Building your own Hello World Container
- 2.3. Using Cloud Run with a custom domain
- 2.4. Triggering a Cloud Run from Cloud Pub/Sub
- 2.5. Deploy a Web application to Cloud Run
- 2.6. Rolling Back a Cloud Run Service Deployment
- 2.7. Cloud Run Gradual Rollouts
- 2.8. Cloud Run Configuration Parameters
- 3. Google App Engine
- 3.1. Deploy a Hello World to App Engine Standard
- 3.2. Deploy a Hello World to App Engine Flexible
- 3.3. Securing your application with Identity-Aware Proxy (IAP)
- 3.4. Custom Domains with App Engine
- 3.5. Using Machine Learning APIs with App Engine
- 3.6. Cube.js and React Dashboards with App Engine
- 3.7. Debugging an Instance
- 3.8. GitLab CI/CD and App Engine
- 4. Google Cloud Compute Engine
- 4.1. Create a Windows Virtual Machine
- 4.2. Create a Linux Virtual Machine and install NGNIX
- 4.3. Connecting to your Windows Virtual Machines with Identity-Aware Proxy TCP Forwarding
- 4.4. Virtual Machine OS Login with 2 Step Verification
- 4.5. Running Startup Scripts
- 4.6. Create a Cluster of NGINX web server with an Instance Group
- 4.7. Deploy Containers to Managed Instance Groups
- 4.8. Transferring Files to your Virtual Machine
- 4.9. Using VM Manager for Patch Management
- 4.10. Backuping your Virtual Machine
- 5. Google Cloud Kubernetes Engine
- 5.1. Create a Zonal Cluster
- 5.2. Create a Regional Cluster
- 5.3. Deploy a MongoDB Database with StatefulSets
- 5.4. Resizing a Cluster
- 5.5. Load Testing with Locust
- 5.6. Multi-Cluster Ingress
- 5.7. Continuous Delivery with Spinnaker and Kubernetes
- 5.8. Deploy a Spring Boot Java Application
- 5.9. Skaffold
- 5.10. GKE Autopilot
- 6. Working with Data
- 6.1. Speeding up GCE Transfers - Multiprocessing
- 6.2. Speeding up GCS Transfers - Parallel Composite Uploads for large files
- 6.3. Adding event timestamps to PubSub
- 6.4. Mounting GCS as a File-System (sort of)
- 6.5. Designing your Schema for Spanner with Interleaved Tables
- 6.6. Automatically archiving and deleting objects on GCS
- 6.7. Locking down Firestore Database so a User can edit only their data
- 7. BigQuery and Data Warehousing
- 7.1. Building a Pivot Table in BigQuery
- 7.2. Adding Partitioned and Clustered columns to an existing Table
- 7.3. Selecting the Top-1 Result (scalably)
- 7.4. De-duplicating Rows in BigQuery with Row Key
- 7.5. De-duplicating Rows in BigQuery with Timestamp
- 7.6. Un-deleting a Table in BigQuery
- 7.7. Streaming JSON or Avro Data into BigQuery with a Dataflow Template
- 8. Data Processing Tools
- 9. AI/ML
- 9.1. Creating an AI Platform Notebook
- 9.2. Training a Python Model Serverlessly
- 9.3. Serving a Python Model with Serverless
- 9.4. Get Explanations with your ML Predictions
- 9.5. Create a Custom Notebook Environment
- 9.6. Tensorflow Batch Predictions on Cloud AI Platform
- 9.7. BQ Data in Tensorflow or Pytorch Model
About the Authors
Rui Costa has worked at Google in various roles, most recently as a Learning Consultant working with strategic customers and partners to create and execute on their Google Cloud learning plans. He has also served as an AI Coach for the Google AI Impact Challenge, where he had the opportunity to speak at multiple conferences including the NetHope Global Summit 2019. He is also the founder of the Speech Analysis Framework, which has successfully graduated to become a Product within Google.
Drew Hodrun has had a number of roles at Google, all customer facing and focused on Machine Learning applications running on Google Cloud. He’s worked primarily in the Autonomous Vehicle, Financial Services, and Media+Entertainment industries. Most recently Drew is a Learning Consultant, working with large strategic customers and building engineer to engineer workshops to meet their toughest training needs. Drew has spoken at a variety of conferences, including Predictive Analytics World 2020, Google Cloud Next ‘18-’19, O’Reilly AI Conference ‘18, and others.
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