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Machine Learning in GCP

Follow this guide to deploy the Machine Learning module for Google Cloud Platform (GCP) private data processing.

Prerequisite

Before you deploy the Machine Learning module, you must complete these steps on the Set Up GCP Project and VPC for Private Data page...

  1. Configured a VPC dedicated to AACAAC as mentioned in the Configure Virtual Private Network section.

  2. Service account and base IAM roles attached to the service account as mentioned in the Configure IAM section.

  3. Successfully triggered private data processing provisioning as mentioned in the Trigger Private Data Handling Provisioning section.

Project Setup

Step 1: Configure IAM

Step 1a: IAM Binding to the Service Account

Assign these additional roles to the aac-automation-sa service account that you created during Set Up GCP Project and VPC for Private Data:

  • Compute Load Balancer Admin: roles/compute.loadBalancerAdmin

  • Compute Instance Admin (v1): roles/compute.instanceAdmin.v1

  • Compute Storage Admin: roles/compute.storageAdmin

  • Kubernetes Engine Cluster Admin: roles/container.clusterAdmin

  • Storage Admin: roles/storage.admin

  • Cloud Memorystore Redis Admin: roles/redis.admin

Step 2: Configure Subnet

Nota

If you purchased Designer Cloud and Machine Learning, then configure the subnets as mentioned in the Designer Cloud setup guide. Both Designer Cloud and Machine Learning resources share the same subnets.

Machine Learning in the private data processing environment requires 2 subnets.

  • aac-gke-node (required): The GKE cluster uses this subnet to execute Alteryx software jobs (connectivity, conversion, processing, publishing).

  • aac-public (required): This group doesn’t run any services, but the gke_node group uses it for egress out of the cluster.

Step 2a: Create Subnets in the VPC

Configure subnets in the aac-vpc VPC.

Follow this example to create subnets with subnet name, subnet size, and other configurations (modify values, as needed, to meet your network architecture).

Subnet Name

Subnet

Secondary Subnet Name

Secondary Subnet Size

Notes

aac-gke-node

10.0.0.0/22

aac-gke-pod

10.4.0.0/14

GKE cluster, GKE pod, and GKE service subnets.

 

aac-gke-service

10.64.0.0/20

 

aac-public

10.10.1.0/25

N/A

N/A

Public egress.

Importante

The subnet IP addresses and sizes in the table are shown as an example. Modify values, as needed, to meet your network architecture. Subnet region must be the region where ‘Private data Handling’ is to be provisioned.

The subnet name MUST match with the name as shown in the table.

Step 2b: Subnet Route Table

Create the route table for your subnets.

Importante

You must configure the Vnet with a network connection to the internet in your subscription.

Nota

This route table is an example.

Address Prefix

Next Hop Type

/22 CIDR Block (aac-gke-node)

aac-vpc

/24 CIDR Block (aac-private)

aac-vpc

/25 CIDR Block (aac-public)

aac-vpc

0.0.0.0/0

<gateway_ID>

Nota

Your <gateway id> can be either a NAT gateway or an internet gateway, depending on your network architecture.

Private Data Processing

Attenzione

Se modifichi o rimuovi qualsiasi risorsa del cloud pubblico fornita da AAC dopo l'attivazione della gestione dei dati privati, si verifica uno stato di non coerenza. Questa incoerenza genera errori durante l'esecuzione del processo o il deprovisioning della configurazione di gestione del piano dati privato.

Step 1: Trigger Machine Learning Deployment

Data processing provisioning triggers from the Admin Console inside AACAAC. You need Workspace Admin privileges within a workspace in order to see it.

  1. From the AACAAC landing page, select the Profile menu and then select Workspace Admin.

  2. From the Admin Console, select Private Data Handling and then select Processing.

  3. Select the Machine Learning checkbox and then select Update.

Selecting Update triggers the deployment of the cluster and resources in the GCP project. This runs a set of validation checks to verify the correct configuration of the GCP project.

Nota

The provisioning process takes approximately 35–40 minutes to complete.

After the provisioning completes, you can view the created resources (for example, VM instances and node groups) through the GCP console. It is very important that you don't modify them on your own. Manual changes might cause issues with the function of the private data processing environment.

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