Google Cloud Codelabs and Challenges
Come learn about Google Cloud Platform by completing codelabs and coding challenges! The following codelabs and challenges will step you through using different parts of Google Cloud Platform. They cover a wide range of topics such as Google Cloud Basics, Compute, Data, Mobile, Monitoring, Machine Learning and Networking. Go to g.co/codelabs/cloud to find more codelabs you can try at home.
- Choose an event
- A Tour of Gemini Code Assist Standard and Enterprise for Developers in Google Cloud Shell Editor
- Accelerating analytical queries with columnar engine in AlloyDB Omni.
- Access files in Cloud Storage with the Spring Resource abstraction
- Advanced Load Balancing Optimizations Codelab
- AlloyDB Omni and Local AI Model on Kubernetes.
- Analyze Clinical Data using BigQuery and AI Platform Notebooks
- Analyze production performance with Cloud Profiler
- Analyzing a financial ML model deployed on Cloud AI Platform with the What-if Tool
- Apache Spark and Jupyter Notebooks on Cloud Dataproc
- Automated Classification of Data Uploaded to Cloud Storage with the DLP API and Cloud Functions
- Battle Jamón - A Microservices Battle Ground
- Battle Jamón Terms and Conditions
- Battle One - A Microservices Battle Ground
- Battle Peach - A Microservices Battle Ground
- Bot Management with Google Cloud Armor + reCAPTCHA
- Buffer HTTP requests with Cloud Tasks
- Build a contextual Yoga Poses recommender app with Firestore, Vector Search and Gemini 2.0 (Java version)!
- Build a Fraud Detection model on Cloud AI Platform with TensorFlow Enterprise and BigQuery
- Build a frontend Django client for a Dialogflow app
- Build a Kotlin Spring Application with Google Cloud Platform
- Build a Patent Search Assistant with AlloyDB and Vertex AI Agent Builder - Part 2
- Build a Quiz Generator with GenAI and Cloud Run
- Build a Slack bot with Node.js on Cloud Run
- Build a Smart Shopping Assistant with AlloyDB and Vertex AI Agent Builder - Part 1
- Build a Smart Shopping Assistant with AlloyDB and Vertex AI Agent Builder - Part 2
- Build an AI-powered outfit recommendation app with AlloyDB and serverless runtimes
- Build an appointment scheduler with Dialogflow
- Build an AutoML Forecasting Model with Vertex AI
- Build an event-driven orchestration with Eventarc and Workflows
- Build and launch a Spring Boot Java app from Cloud Shell
- Build and launch an ASP.NET Core app from Google Cloud Shell
- Build Voice Bots for Android with Dialogflow Essentials & Flutter
- Build, train, and deploy an XGBoost model on Cloud AI Platform
- Building a financial ML model with the What-If Tool and Vertex AI
- Building a gRPC service with C#
- Building a gRPC service with Java
- Building a Serverless Data Pipeline: IoT to Analytics
- Building an LLM and RAG-based chat application using AlloyDB AI and LangChain
- Building an LLM and RAG-based chat application using Cloud SQL databases and LangChain
- Building MLOps Workflows with Airflow 2 on GKE
- Cache data from a Spring Boot app with Memorystore
- Calculate Pi on Compute Engine
- Call APIs from a Google Cloud project
- Cloud Armor and TCP/SSL Proxy Load Balancers - Rate limiting and IP Deny list Codelab
- Cloud Armor and TCP/SSL Proxy Load Balancers - Rate limiting and IP Deny list Codelab
- Cloud Armor for NLB/VM with User Defined Rules
- Cloud Armor NamedIP List
- Cloud Armor Preconfigured WAF Rules Codelab
- Cloud Function to Automate CSV data import into Google Sheets
- Cloud IDS
- Cloud Spanner with Hibernate ORM
- Cloud Spanner with Terraform
- Cloud Spanner: Create a gaming leaderboard with C#
- Cloud Spanner: Create a gaming leaderboard with Go
- Cloud Spanner: Create a gaming leaderboard with Java
- Cloud Spanner: Your First Database
- Connect a Spring Boot app to Cloud SQL
- Connect AlloyDB to Oracle through Google VPN
- Connecting to Cloud SQL: Compute Engine, Private IP and Cloud SQL Proxy
- Connecting to Cloud SQL: Public IP and authorized networks
- Containerize a Spring Boot Kotlin app and deploy it to Cloud Run
- Continuous deployment to Google Kubernetes Engine (GKE) with Cloud Build
- Create a Generative Chat App with Vertex AI Conversation
- Create a transcript of your business meetings using Google Docs & Machine Learning
- Create Custom Visualizations in Looker Studio
- Create Data Studio Community Visualizations with dscc-gen
- Defending Edge Cache with Cloud Armor
- Deploy a basic "Google Translate" app on Python 2 App Engine
- Deploy a basic "Google Translate" app on Python 2 Cloud Run (Docker)
- Deploy a basic "Google Translate" app on Python 3 Cloud Functions
- Deploy a basic "Google Translate" app on Python 3 Cloud Run (Docker)
- Deploy a basic "Google Translate" Express.js app on App Engine, Cloud Functions, and Cloud Run
- Deploy a Lustre Parallel File System on GCP
- Deploy a Micronaut application containerized with Jib to Google Kubernetes Engine
- Deploy a Spring Boot app to App Engine standard environment
- Deploy a Spring Boot Java app to Kubernetes on Google Kubernetes Engine
- Deploy a website with Cloud Run
- Deploy an ASP.NET Core app to App Engine
- Deploy an Auto-Scaling HPC Cluster with Slurm
- Deploy and run a container with Cloud Run on Node.js
- Deploy and Update a .NET Core app in Google Kubernetes Engine
- Deploy ASP.NET app to Windows Server on Compute Engine
- Deploy ASP.NET Core app to Google Kubernetes Engine with Istio (Part 1)
- Deploy ASP.NET Core app to Google Kubernetes Engine with Istio (Part 2)
- Deploy ASP.NET Core app to Kubernetes on Google Kubernetes Engine
- Deploy Windows Server with ASP.NET Framework to Compute Engine
- Deploy, scale, and update your website with Google Kubernetes Engine (GKE)
- Dialogflow CX: Build a retail virtual agent
- Distributed tracing with Spring Cloud Sleuth and Cloud Trace
- Document AI Workbench - Custom Document Extractor
- Document AI Workbench - Uptraining
- Document AI: Human in the Loop
- Doing a Google Cloud codelab? Start here!
- Encrypt and decrypt data with Cloud KMS
- Encrypt and decrypt data with Cloud KMS (Asymmetric)
- Encrypt Cloud Functions using Customer-managed Encryption Keys (CMEK)
- Engage users with your Action for Google Assistant
- Events for Cloud Run for Anthos Codelab
- Explaining a fraud detection model with Cloud AI Platform
- Explicit Chaining of GCP L7 Load Balancers with PSC
- Extending support for App Engine bundled services: Part 1 (Module 17)
- External HTTPs LB with Advanced Traffic Management (Envoy) Codelab
- External HTTPs LB with Advanced Traffic Management (Envoy) Codelab
- Form Parsing with Document AI (Python)
- FraudFinder: From raw data to AI with Vertex AI and BigQuery.
- From Notebook to Kubeflow Pipelines with HP Tuning: A Data Science Journey
- From Notebook to Kubeflow Pipelines with MiniKF and Kale
- Gemini in Java with Vertex AI and LangChain4j
- Generative AI powered chat with users and docs in Java with PaLM and LangChain4J
- Generative AI text generation in Java with PaLM and LangChain4J
- Get predictions from a pre-trained TensorFlow image model on Vertex AI
- Getting started with App Engine (Python 3)
- Getting Started with BigQuery ML
- Getting started with Cloud Functions
- Getting started with Cloud Functions (2nd gen)
- Getting started with Cloud Run jobs
- Getting Started with Cloud Shell & gcloud
- Getting started with Managed Active Directory
- Getting started with Vector Embeddings in Cloud SQL for PostgreSQL
- Getting started with Vector Embeddings with AlloyDB AI
- Google Cloud Functions in C#
- Google Compute Engine
- Grant access to your project with IAM
- Hands-on: Create a TV guide Google Chat with Google Workspace and Dialogflow
- Hello Cloud Run with C#
- Hello Cloud Run with Python
- Host and scale a web app in Google Cloud with Compute Engine
- How to integrate Dialogflow with BigQuery
- How to Interact with APIs Using Function Calling in Gemini
- How to Transact Digital Assets with Multi-Party Computation and Confidential Space
- How to use App Engine blobstore (Module 15)
- How to use App Engine Memcache in Flask apps (Module 12)
- How to use App Engine Task Queue (pull tasks) in Flask apps (Module 18)
- How to use App Engine Task Queue (push tasks) in Flask apps (Module 7)
- HTTP Cloud Functions in Python
- Image archiving, analysis, and report generation Google Workspace & Google Cloud
- Increase intent coverage and handle errors gracefully with generative fallback
- Informed decision making using Dialogflow CX generators and data stores
- Ingest CSV (Comma-separated values) data to BigQuery using Cloud Data Fusion - Real time ingestion
- Ingest CSV data to BigQuery using Cloud Data Fusion - Batch ingestion
- Ingest FHIR (Fast Healthcare Interoperability Resources) to BigQuery
- Install and use Cloud Tools for PowerShell
- Install and use Cloud Tools for Visual Studio
- Installing and Setting-up Toolbox for your Gen AI & Agentic Applications on AlloyDB
- Instrument trace information using OpenTelemetry
- Integrate the Vision API with Dialogflow
- Integrating Dialogflow with Google Chat
- Intro to Vertex Pipelines
- Introduction to Cloud Bigtable
- Introduction to Cloud Operations Suite
- Introduction to Query Insights for Cloud SQL
- Introduction to serverless orchestration with Workflows
- Introduction to testing with Gemini Code Assist
- Kubeflow Pipelines - GitHub Issue Summarization
- Learn how to build and deploy a LangChain app on Cloud Run
- Learn how to invoke authenticated Cloud Functions
- Learn how to invoke authenticated Cloud Run functions
- Load and query data with the bq command-line tool for BigQuery
- Local Development with Cloud Functions for Node.js using Visual Studio Code
- Make the Most of Experimentation: Manage Machine Learning Experiments with Vertex AI
- Managing Document AI processors with Python
- Messaging with Spring Integration and Google Cloud Pub/Sub
- Microservice Rainbow Rumpus
- Migrate a Python 2 App Engine Cloud NDB & Cloud Tasks app to Python 3 and Cloud Datastore (Module 9)
- Migrate from App Engine Blobstore to Cloud Storage (Module 16)
- Migrate from App Engine Memcache to Cloud Memorystore (Module 13)
- Migrate from App Engine Task Queue pull tasks to Cloud Pub/Sub (Module 19)
- Migrate from App Engine Task Queue Push Tasks to Cloud Tasks (Module 8)
- Migrate from App Engine Users service to Cloud Identity Platform (Module 21)
- Migrating a Monolithic Website to Microservices on Google Kubernetes Engine
- Migrating from Compute Engine to Kubernetes Engine with Migrate for Anthos
- Migrating from Google App Engine Java app to Cloud Run with Buildpacks
- Migrating from Google App Engine Java app to Cloud Run with Docker
- Migrating from Google App Engine Java app to Cloud Run with Jib
- Migration from Cassandra to Bigtable with a Dual-Write Proxy
- Module 11: Migrating from Google App Engine to Cloud Functions
- Module 1: Migrate from App Engine webapp2 to Flask
- Module 2: Migrate from App Engine ndb to Cloud NDB
- Module 3: Migrate from Google Cloud NDB to Cloud Datastore
- Module 4: Migrate from Google App Engine to Cloud Run with Docker
- Module 5: Migrate from Google App Engine to Cloud Run with Cloud Buildpacks
- Module 6: Migrate from Cloud Datastore to Cloud Firestore
- Optical Character Recognition (OCR) with Document AI (Python)
- Optimizing the price of retail products
- Partitioning and Clustering in BigQuery
- Per-Instance Weighted Network Load Balancing
- Pic-a-daily: Lab 1—Store and analyse pictures
- Pic-a-daily: Lab 1—Store and analyse pictures (Java)
- Pic-a-daily: Lab 2—Create thumbnails of pictures
- Pic-a-daily: Lab 3—Create a collage of most recent pictures
- Pic-a-daily: Lab 4—Create a web frontend
- Pic-a-daily: Lab 5—Cleanup after image deletion
- Pic-a-daily: Lab 6—Orchestration with Workflows
- Preprocessing BigQuery Data with PySpark on Dataproc
- Private Service Connect for Google APIs
- Private Service Connect for Google APIs
- Prototype to Production: Distributed training on Vertex AI
- Prototype to Production: Getting predictions from custom trained models
- Prototype to Production: Hyperparameter tuning
- Prototype to Production: Training custom models with Vertex AI
- Prototyping models in AI Platform Notebooks
- Provisioning and Using a Managed Hadoop/Spark Cluster with Cloud Dataproc (Command Line)
- PySpark for Natural Language Processing on Dataproc
- Query the Wikipedia dataset in BigQuery
- Rate Limiting with Cloud Armor
- Retrieving Credentials/Secrets from Secret Manager with Spring Boot
- Route Datadog monitoring alerts to Google Cloud with Eventarc (Part 1)
- Route Datadog monitoring alerts to Google Cloud with Eventarc (Part 2)
- Run a big data text processing pipeline in Cloud Dataflow
- Running BigQuery jobs in parallel with Workflows
- Running custom model training on Vertex Pipelines
- Running your first SQL statements using Google Cloud Dataflow
- Search for and select Google APIs
- Secure shared data in use with Confidential Space
- Securing ML models and Intellectual Property using Confidential Space
- Securing Your GKE Deployments with Binary Authorization
- Serverless Web APIs Workshop
- Set up and navigate your first Google project
- Sign and verify data with Cloud KMS (Asymmetric)
- Signed container image codelab
- Specialized Processors with Document AI (Python)
- Spring Boot application with Cloud Datastore
- Spring Boot application with Cloud Spanner
- Spring Native on Google Cloud
- TCP Proxy Codelab - Rate limiting and IP Deny list with TCP Proxy Load balancer
- TensorFlow, Keras and deep learning, without a PhD
- Time Series Forecasting with Vertex AI and BigQuery ML
- Toy Store Search App with Cloud Databases, Serverless Runtimes and Open Source Integrations
- Training and hyperparameter tuning a PyTorch model on Cloud AI Platform
- Transform and Load Google Forms Survey Responses into BigQuery
- Transitioning a network load balancer from target pools to regional backend services
- Trigger Cloud Run with Eventarc events
- Trigger Kubernetes services with Eventarc events
- Troubleshoot with Gemini CodeLab
- Trusted Space codelab
- Understand entities in Dialogflow
- Understand fulfillment by integrating Dialogflow with Calendar
- Use BigQuery to query GitHub data
- Use Confidential Space with protected resources that aren't stored with a cloud provider
- Use Stackdriver Logging and Stackdriver Trace for Cloud Functions
- User authentication with Identity-Aware Proxy
- Using BigQuery with C#
- Using BigQuery with Node.js
- Using BigQuery with Python
- Using Cloud NAT Dynamic Port Allocation
- Using Cloud NAT NAT rules
- Using Document AI Warehouse to Ingest, Process, and Search Documents
- Using External HTTP(s) Hybrid load balancer to reach a Network Endpoint Group
- Using Gemini Code Assist to explore and enhance AI Summarization Jump Start Solution
- Using Notebooks with Google Cloud Dataflow
- Using Private Service Connect to publish and consume services with GKE
- Using Secret Manager with Python
- Using the Natural Language API with C#
- Using the Natural Language API with Python
- Using the Speech-to-Text API with C#
- Using the Speech-to-Text API with Node.js
- Using the Speech-to-Text API with Python
- Using the Text-to-Speech API with C#
- Using the Text-to-Speech API with Node.js
- Using the Text-to-Speech API with Python
- Using the Translation API with C#
- Using the Translation API with Python
- Using the Video Intelligence API with C#
- Using the Video Intelligence API with Python
- Using the Vision API with C#
- Using the Vision API with Python
- Using Vertex ML Metadata with Pipelines
- Vertex AI Workbench: Build an image classification model with transfer learning and the notebook executor
- Vertex AI Workbench: Train a TensorFlow model with data from BigQuery
- Vertex AI: Building a fraud detection model with AutoML
- Vertex AI: Co-host models on the same VM for predictions
- Vertex AI: Custom training job and prediction using managed datasets
- Vertex AI: Distributed hyperparameter tuning
- Vertex AI: Export and deploy a BigQuery Machine Learning Model for Prediction
- Vertex AI: Hyperparameter Tuning
- Vertex AI: Multi-Worker Training and Transfer Learning with TensorFlow
- Vertex AI: Training and serving a custom model
- Vertex AI: Use autopackaging to fine tune Bert with Hugging Face on Vertex AI Training
- Vertex AI: Use custom prediction routines with Sklearn to preprocess and postprocess data for predictions
- Visualize Clinical Data using Looker
- Visualizing your BigQuery Data in Data Studio
- VPC Service Controls Basic Tutorial I
- VPC Service Controls Basic Tutorial II - Troubleshooting Egress Violation
- Writing Synthetic Monitoring Tests for your services using Gemini
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