Get started with Google Kubernetes Engine — create clusters, deploy, scale and run workloads on Kubernetes
Analyze large datasets with BigQuery — write SQL, manage datasets and control query costs
Run containers serverlessly on Cloud Run — deploy, auto-scale and pay per use with no servers to manage
Call Gemini models via API — text, multimodal, and tool/function calling in your code
Build apps fast with Firebase — auth, Firestore, hosting and functions all in one
Get started with the Google Ads API — manage campaigns, pull reports and automate ads
Use the gcloud CLI to manage Google Cloud resources — create, configure and operate from the command line
Get started with Cloud SQL — managed MySQL/PostgreSQL databases with automatic backups
Build ML models with SQL in BigQuery — predict and analyze without moving the data out
Connect the Gemini Live API for real-time voice/video conversations with the model
Secure GKE — RBAC, network policy and workload identity on Kubernetes
Auto-generate Cloud Logging queries — find the logs you need faster
Pull data from Google Analytics via the Data API — traffic and user-behavior reports
Start adding Google Mobile Ads (AdMob) to mobile apps — banner, interstitial and rewarded
Use AlloyDB — Google Cloud's high-performance PostgreSQL database
Use Bigtable — a large-scale, low-latency NoSQL database
Manage GKE networking — services, ingress and network policy
Set up monitoring/logging/tracing for GKE clusters — spot problems fast
Cut GKE costs — right-size, autoscale and use spot nodes
Pick the right metrics to watch in Cloud Monitoring — set alerts that matter
Build AI agents with Google's Gemini Agents API
Run AI inference on GKE cost-effectively and at scale
A golden path to set up GKE production-ready
Manage GKE storage — persistent volumes and storage classes
Upgrade GKE clusters safely without downtime
Share GKE across teams/tenants with strong, secure isolation
Onboard an app to GKE for the first time — deploy, configure, expose services
Make GKE workloads resilient — health checks, PDBs and autoscaling
Scale GKE workloads to match load without waste
Use BigQuery DataFrames (BigFrames) to analyze data with Python
Use the Gemini Interactions API to build interactive user experiences
Run batch/HPC jobs on GKE — queues and heavy compute
