Google Cloud

As explained in Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

Google Cloud is a cloud-computing platform providing infrastructure and managed services for applications, data, and machine learning.

Google Cloud provides an alternative to local deployment for applications and machine-learning models. It supplies managed infrastructure for projects, storage, endpoints, monitoring, resiliency, and other MLOps needs. section 10.0 section 10.2

A practical deployment path includes:

• Create a Google Cloud project to organize work and control access to resources. section 10.3.1
• Create a Google Cloud Storage bucket as a data-storage container. section 10.3
• Use Vertex AI to deploy a trained model through an endpoint. section 10.0
• Choose the Cloud console, command-line interface, or client libraries to interact with services. section 10.3.1

From local prediction app to managed endpoint

A Kuala Lumpur real-estate price model can first run with its web server locally, then move to a Vertex AI endpoint. Google Cloud addresses production concerns such as model versions, monitoring, retraining, redeployment, uptime, and resiliency. section 10.0 section 10.2

Figure 10.7 Google Cloud console

Use the Google Cloud console view to orient the first project and service-management steps. section 10.3.1

How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it

How deep it goes

Works through project setup, billing, storage, interfaces, and Vertex AI deployment, while deferring advanced retraining features.

Examples

Uses a Kuala Lumpur real-estate predictor, a local web application, a storage bucket, and a Vertex AI endpoint.

What it assumes

Assumes a trained deep-learning model and introduces account access, billing, projects, and cloud interfaces from the beginning.

What you can do after

Operate/configure a Google Cloud project, storage bucket, console, and Vertex AI endpoint for model deployment.

How it connects

Connects public clouds with machine learning operations, Vertex AI, Google Cloud Storage, model deployment, and generative AI.

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Related concepts

• AWSalternative to · Amazon Web Services (AWS) is Amazon's public cloud computing platform, providing on-demand infrastructure and managed services over the internet.
• cloud computingkind of · Cloud computing is the on-demand delivery of computing resources and services over a network from remotely managed infrastructure.
• cloud providerkind of · A cloud provider is an organization that delivers computing resources, platforms, or managed services over a network, typically on demand.
• Crossplane providerused by · A Crossplane provider is a Crossplane controller package that observes Kubernetes resources and manages corresponding resources in an external infrastructure system.
• Gemini for Google Cloudhas part · Gemini for Google Cloud is Google Cloud's generative-AI assistance and development capabilities based on Gemini models.
• Dockeruses · Docker is a platform and ecosystem for building, distributing, and running applications in containers.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI. The section links point to the parts of the book it draws on.