Compute on Google Cloud Platform

1

Introducci贸n al curso de Google Cloud for Developer Community

2

Lectura: introducci贸n de instalaci贸n

3

Tutorial de Qwiklabs

4

C贸mputo en la nube de Google

5

Opciones de c贸mputo en la nube

6

M谩quinas virtuales a profundidad

7

Tutorial para instalar Qwiklabs

8

Demo: m谩quinas virtuales a profundidad

9

C贸mputo sin administraci贸n con plataformas como servicio

10

Demo: c贸mputo sin administraci贸n

11

Lectura: 驴qu茅 son los contenedores?

12

C贸mputo contenerizado con App Engine Flex

13

C贸mputo contenerizado con Cloud Run

14

Funciones serverless

Continuous Integration, Continuous Delivery

15

CI/CD en Google Cloud Platform

16

Estrategias de Despliegue

17

Repositorios de c贸digo

18

Construcci贸n y despliegue de artefactos

19

Infraestructura como c贸digo

20

Despliegue en Servicios Serverless

Google Kubernetes Engine

21

Kubernetes Overview

22

Demo Kubernetes

23

Planeaci贸n de tu despliegue

24

Anthos

25

Cloud Run for Anthos

26

Demo Cloud Run for Anthos

27

Anthos Service Mesh

28

Site Reliability Engineering con Anthos

Streaming Data Analytics

29

Integraci贸n de datos e ingesta de datos totalmente administrada sobre GCP

30

Demo: ingesta de datos

31

Ingesta de datos confiable en streaming sobre GCP

32

Demo: ingesta de datos confiable

33

Demo: configuraci贸n de Apache Kafka

34

Visualizaci贸n de mensajes de una base de datos relacional en Google Cloud

35

Data Warehouse: el modelo tradicional para construir un repositorio de datos empresarial

36

Data Lakehouse: el nuevo y moderno enfoque para construir un repositorio de datos empresarial

37

El portafolio de gesti贸n de datos en Google Cloud

38

Desglose del portafolio de gesti贸n de datos (Bases de datos) en Google Cloud

39

Gobierno de datos de punta a punta para garantizar la seguridad en tu Data Lake

40

Gobierno de datos: calidad y monitoreo

Machine Learning

41

驴Qu茅 es ML y AI?

42

Plataforma de AI en GCP

43

Auto ML con datos estructurados

44

Demo Auto ML con datos estructurados

45

Predicci贸n de tarifas usando AI notebooks

46

Demo predicci贸n de tarifas usando AI notebooks

47

TensorFlow Extended

Sesiones en vivo

48

Sesi贸n en vivo con Pablo P茅rez Villanueva

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Demo Cloud Run for Anthos

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Resources

How to launch a service on Cloud Run?

Launching a service in Cloud Run on Google Kubernetes Engine (GKE) is a process that may seem complex, but with the right tools and configurations, it becomes an affordable task. This tutorial will guide you step-by-step to deploy your application using Cloud Run, optimizing with features like autoscaling and load balancing. Let's dive into how to run this operation efficiently and straightforwardly.

How do I set up the environment for Cloud Run?

First, it's essential to get our infrastructure ready. Before launching your application, make sure you have configured the Kubernetes cluster. Here we show you the fundamental command to create it:

gcloud container clusters create [CLUSTER_NAME] \ --addons=HorizontalPodAutoscaling,CloudRun \ --enable-ip-alias \ --enable-stackdriver-kubernetes \ --machine-type=n1-standard-2\ --zone=us-central1-a
  • HorizontalPodAutoscaling: Essential to use Cloud Run.
  • CloudRun: Allow our applications to run on this platform.
  • enable-ip-alias and enable-stackdriver-kubernetes: Tools to monitor and manage the cluster.
  • machine-type: Defines the type and size of the virtual machine.
  • zone: The geographical location for resource and latency optimization.

How to deploy a service in the cluster?

With the cluster prepared, we move on to the deployment of the service. Follow these steps:

gcloud run deploy my-service \ --namespace default \ --image gcr.io/[PROJECT]/cloud-run-hello \ --platform gke \ --cluster PlatziCluster \ --cluster-location us-central1-a
  • my-service: Is the name given to your service.
  • namespace: Indicates the workspace, by default used here.
  • image: Container image to use, registered in Google Container Registry.
  • platform and cluster: Specify where the service will be launched.
  • cluster-location: Specific location in the cluster.

How to verify that the service is active?

Once deployed, it is important to verify that your service is working properly. This can be done through a simple curl command:

curl -o output.html [SERVICE_URL].

If executed successfully, you will get an HTML file that you can open in a browser to visualize your service in action.

Congratulations! If you followed these steps, you have successfully deployed a Cloud Run service on GKE. Remember that each deployment generates a YAML that you can reuse for future deployments, thus facilitating the management and scalability of your cloud applications. Continue to explore and experiment with new configurations to optimize your projects.

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Excelente demo, muy sencillos los comando de GCP

muy practico

facil