The Ultimate Beginners guide to Google Cloud Platform (GCP)

Explore Google Compute Engine, an infrastructure-as-a-service that lets you create and manage virtual machines, customize images and disks, and scale across regions with instance groups.

Create and connect to a Linux VM on Google Cloud, enable the Compute Engine API, configure region and machine type, and use the browser-based connection to upload and download files.

Create a Windows VM on Google Cloud Platform using Compute Engine, then connect via remote desktop to transfer files and set up a web server.

Explore google cloud storage, including object, blob, archival, and file storage, with lifecycle management, multi-region redundancy, unlimited storage, worldwide accessibility, and data transfer via transfer service and appliance.

Explore using Google Cloud Storage to create buckets, manage permissions, and transfer files to a Linux VM via gsutil, while setting up a Compute Engine instance.

Explore Google BigQuery, a fully managed, petabytes-scale analytics data warehouse on Google Cloud with near real-time queries and no infrastructure to manage, including public datasets and creating and deleting datasets.

Explore a live demo of BigQuery in the Google Cloud Console. Learn to query public datasets, load and create datasets and tables, and write queries with aggregation and ordering.

Discover Google Kubernetes Engine, a managed environment for deploying and scaling containerised applications on Google Cloud, with clusters, load balancing, node pools, automatic scaling, upgrades, and monitoring.

Demonstrates Google Kubernetes Engine basics: enable APIs, configure a project, create a cluster, deploy a hello app, expose with a load balancer, and clean up.

Explore Google App Engine, a managed serverless platform for developing and hosting scalable web apps, with zero maintenance, automatic scaling, language flexibility, security, and pay only for what you use.

Learn how to deploy a simple application on Google App Engine using Google Cloud Platform, including creating a project, installing the Cloud SDK, enabling APIs, deploying, and retrieving logs.

Discover Google Cloud Run, a fully managed serverless platform that runs containers on demand, with zero infra management, supports any language, and scales to zero with pay-for-use pricing.

Watch a practical demo of Google Cloud Run, deploying a prepackaged container, enabling the Cloud Run API, configuring auto scaling, and allowing unauthenticated traffic to run and scale on demand.

Explore Google Cloud Build, a cloud-native ci/cd service that builds code from cloud storage or repos, runs steps in Docker containers, and outputs artifacts such as Docker images and archives.

Learn to set up and run builds with Google Cloud Build, enable API services including Artifact Registry, install the Google Cloud SDK, and run builds using quickstart files.

Explore Google Cloud Operations, a suite for monitoring, logging, and diagnostics. Learn to collect metrics, manage logs, and use error reporting to surface issues quickly.

Set up a Google Cloud Compute Engine VM, install Apache and the Cloud Monitoring and Logging agents, and configure uptime checks with an alerting policy and view logs.

Explore cloud logging with a hands-on demo: write and list log entries via the cloud command line tool, view and query logs with log explorer and the logging API.

Learn to use Google Cloud Error Reporting to monitor and view error details across apps, configure notifications, and see a demo of generating errors in a deployed App Engine project.

Explore Google Firestore's document model with collections and nested objects, learn flexible queries and indexing, and see how real-time updates and scalable cloud architecture support high-performance apps.

Create a new Google Cloud project, enable Firestone, and build a sample database by adding a cities collection with documents like New York and Boston, including name and population fields.

Explore Google Pub/Sub, a fully managed real-time messaging service for publish and subscribe, enabling asynchronous data ingestion, streaming analytics, and enterprise-wide event distribution across Google systems.

See a hands-on demo of Google Pub/Sub, creating a topic and subscriptions, and publishing messages. Learn how to view messages, manage retention, and configure acknowledgement deadlines.

Explore Google Cloud Functions as serverless compute that runs single-purpose functions in response to cloud events, with triggers, multiple runtimes, automatic scaling, and use cases like data processing and APIs.

Learn how to create, deploy, and test a simple node cloud function in Google Cloud Functions, trigger it via events or requests, and review logs and resources.

Explore the Google Cloud Vision API, including image labeling, landmark and face detection, logo recognition, and OCR. Learn text extraction, explicit content tagging, and a cloud storage bucket demo.

Demonstrates a Cloud Vision API demo: create a Cloud Storage bucket, upload an image, make it public, and test the API with the API Explorer to retrieve image labels.

Explore speech-to-text concepts on Google Cloud Platform with synchronous, asynchronous, and streaming recognition using REST or gRPC, enabling real-time transcription.

Demonstrates using the Google Cloud Speech-to-Text API to transcribe audio, enabling the API, using the Cloud SDK or console, and verifying a transcript with 98 percent confidence.

Explore the cloud natural language API, including sentiment analysis, entity analysis, syntax analysis, and content classification. Learn to annotate text with the API and review a cloud console demo.

Explore a hands-on demo of the Cloud Natural Language API on Google Cloud Platform, including enabling the API and analyzing text using Cloud Shell to identify entities and content.

Explore AutoML Tables on Vertex II to automatically build and deploy supervised learning models from structured data, using targets and features through a standard workflow from gathering data to predicting.

Watch a practical AutoML Tables demo on Vertex AI using the bank marketing dataset: create a tabular dataset, train a deposit-prediction model, deploy an endpoint, and test predictions.

Discover how AutoML Natural Language builds custom models to classify documents, extract entities, and analyze sentiment, then train on your data and analyze text with configurable categories and sentiment scores.

Explore AutoML natural language on Vertex AI by creating a text classification dataset, training a single-label model, deploying an endpoint, testing predictions, and cleaning up resources.

Learn how AutoML translation customizes domain-specific content with high-quality training data, contrasting it with general translation API, and explore data preparation, validation, and cost considerations.

Explore auto translation in Google Cloud's translation service. Learn steps to create datasets, train models, evaluate, and use the API or UI for Spanish to English translation.

Discover how the video intelligence api analyzes videos from local, cloud storage, or live livestream, with explicit content detection, face detection, logo recognition, label annotation, speech transcription, and text detection.

Experience a hands-on demo of the video intelligence API by uploading a video to a Google Cloud Storage bucket and analyzing detections like person, pedestrian, and road with timestamps.

Learn how to train and deploy image classification models with AutoML Vision, from labeled data and human labeling to mobile deployment on iOS/Android via Firebase, with a hands-on flowers demo.

Demonstrates setting up AutoML Vision on Google Cloud, creating a single-label flower dataset from Cloud Storage, training and deploying a model, testing with sample images, and cleaning up resources.

Explore automatic video intelligence, including classification and object tracking, and learn to train models that label and track shots with your own custom labels, including a demo annotating videos.

Explore a demo of automatic video intelligence with AutoML Video Classification, from enabling APIs and creating a dataset to training a model, testing predictions, and managing cloud storage buckets.

Explore serverless workflows on Google Cloud Platform, linking Cloud Functions, Cloud Run, and external APIs via YAML or JSON definitions, with scalable, pay-as-you-go execution and built-in error handling.

Watch a hands-on demo of creating, deploying, and executing a workflow in the Google Cloud Console that fetches the current date, reads Wikipedia facts, and returns the public API response.

Explore cloud source repositories in Google Cloud, learn to create private repositories for collaborative version control, connect to GitHub or Bitbucket, push and deploy a Python app to App Engine.

Explore Cloud Source Repositories by creating a Hello World repository, pushing code, deploying to App Engine, and redeploying after edits, with a hands-on walkthrough of viewing changes and clean-up.

Explore Google Cloud Marketplace to quickly deploy software packages that run on Google Cloud, like Compute Engine, scale deployments, customize VM configurations, and manage updates with support options.

Explore a demo of Google Cloud Marketplace, showing two ways to launch software like Red Hat Enterprise Linux 8 on Compute Engine, with separate compute and software costs.

Discover secret manager, which stores, manages, and accesses secrets as binary blobs or text strings with permissions, versions, and rotation, and compare it to Kamus with pricing details.

Explore a hands-on demo of Google Cloud Secret Manager: enable the service, create secrets, manage versions, set expiry and rotation, and delete or disable secrets.

Reset the cloud shell to its default state by deleting created files and restarting, with optional reason, to return to a clean cloud shell.

Explore the basics of the Google Cloud platform with $300 in credits and free monthly limits, and discover scalable, powerful services to learn and experiment with.