Google Cloud AI platform pipelines is a service that helps deploy robust AI and ML pipelines. It also offers monitoring, version tracking, reproducibility, and auditing in the cloud. Google mentioned that it’s an easy-to-install environment for machine learning workflows that will reduce the time spent on bringing workflows to production.
Google pipelines have two major parts: a pipeline tool for building, developing, and sharing pipelines and an infrastructure for deploying and running structured ML workflows integrated with Google Cloud platform services.
In this blog, you will learn about Google Cloud, the Google Cloud AI platform, and how to deploy Google pipelines in Google Cloud.
Table of Content
Prerequisites
Understanding of Pipelines
What is Google Cloud
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Google Cloud is a suite of cloud computing services that operates on Google’s infrastructure for all its user products, such as Gmail, Google Drive, Google Search, and YouTube. Google Cloud Platform offers a wide range of products and services for storage, computing, and application development. Cloud administrators, software developers, and enterprise IT professionals can access Google Cloud services through a dedicated network connection or the public Internet.
Key Features of Google Cloud
Let us look at the features of the Google Cloud Platform for end-users:
On-Demand Services: These are various web-based tools that work in an automated environment and don’t require human intervention to control any of these tools.
Resource Pooling: On the Google Cloud platform, one can easily access a shared pool of computing resources available to the users as an on-demand feature.
Broad Network Access: Through the broad network access feature of Google Cloud, users can access all the resources and information available on a common platform from any location.
Measured Services: Users only have to pay for the services they opt for.
Elastic: The Google Cloud platform is widely appreciated for its incredible flexibility and elasticity. You can access resources whenever you need them and as per requirement.
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What is the Google Cloud AI platform?
The Google Cloud AI platform is a code-based development environment that enables data engineers, data scientists, and developers to deploy ML models quickly and cost-effectively. The tech stack of the AI platforms supports two SDKs: TensorFlow Extended (TFX) Pipeline SDK and the Kubeflow Pipelines (KFP) SDK.
The two significant benefits of using Google Cloud Platforms are that they can be easily accessed via the AI platform panel and offer safe and authentic access to pipelines without setting up port-forwarding.
Components of AI Platform:
- Training Service
- Prediction Service
- Data labeling service
Tools to interact with AI Platform:
- Google Cloud console
- The Google Cloud CLI
- REST API
- Vertex AI Workbench user-managed notebooks
- Deep Learning VM
Here are some of the features of the Google AI platform:
- Comprehensive integration with other services of Google Cloud such as Big Query, Dataflow, and others.
- The Google Cloud Pipeline components can be customized according to your needs.
- Various characteristic features include pipeline versioning, automatic metadata tracking, and Cloud Logging. These are exclusively termed as Enterprise features for Machine Learning Work Loads.
- With the TFX templates, one can build their own Machine Learning Pipelines.
- The Google Cloud AI Platform pipelines support automatic artifacts and lineage tracking.
- There have been plenty of new features in the platform with the recent release of KubeFlow Pipelines, such as Python Functions.
Understanding Google Pipelines in Google AI Platform
Google Cloud AI Platform Pipelines is an environment for managing and automating machine learning (ML) workflows. Google pipelines offer an easy-to-install environment for designing and deploying ML workflows. It leverages open-source technologies such as Kubeflow Pipelines (KFP) and TensorFlow Extended (TFX). Google Pipelines run on a Google Kubernetes Engine (GKE) cluster where TFX is an abstraction layer and KFP is the orchestrator.
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With Google Pipelines, users can orchestrate their ML workflows as reproducible and reusable pipelines. It saves you the difficulty of setting up Kubeflow Pipelines with Google Kubernetes Engine and TensorFlow Extended.
There are three options for deploying AI Platform Pipelines on GKE:
- Creating a new GKE cluster with AI Platform Pipelines (full access to GC) and deploying Kubeflow pipelines onto this cluster.
- Creating a new GKE cluster (granular access to GC) and deploying Kubeflow pipelines onto this cluster.
- Reusing an existing GKE cluster.
You must ensure that your existing GKE cluster meets the following requirements:
- Must have at least three nodes.
- Each node must have at least 4 GB of memory and 2 CPUs.
- The access scope of your cluster must grant full access to all Cloud APIs.
- The cluster must not already have Kubeflow Pipelines installed.
Benefits of Using Google Pipelines
- Easy installation and management: Google cloud platform is easily accessible through the Cloud Console. The installation process is quick and lightweight, and the pipeline runs on a GKE cluster. You can use an existing cluster, or a cluster is automatically created when installing the pipeline. With the Cloud AI Platform UI, users can easily view and manage their clusters.
- Easy authenticated access: Users get authenticated and secure access to pipelines UI via the Google cloud platform and don’t have to set up port forwarding. It’s also easy to give other team members access to pipelines and components. You can access a pipeline cluster via the rest API service, making it easy to use pipeline SDK from AI platform notebooks.
- Pipelines versioning: Google cloud platform supports pipeline versioning. It allows users to upload multiple versions of the same pipeline in the UI to manage semantically related workflows.
- Build your ML pipeline with TFX templates: TFX SDK offers templates and step-by-step guidance for building an ML pipeline with your data. You can also add components and iterate on them. Users can access TFX templates from AI Platform Pipelines Getting Started page in the Cloud Console.
What is Kubeflow?
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Kubeflow is an open-source machine learning project that enables easy and quick deployment of ML projects and processes on Kubernetes. Since Kubeflow is cloud-agnostic, you can host it on any Kubernetes enables platform like GCP.
Kubeflow pipelines allow you to compose, deploy, and manage machine learning workflows. Users can manage end-to-end orchestration of ML pipelines and run their workflows in hybrid or multiple environments. Kubeflow supports collaboration, visualization, and reproducibility in ML workflow life cycles.
Kubeflow’s components include:
- Support for distributed TensorFlow training via the TFJob CRD.
- The ability to serve trained models using TensorFlow Serving.
- A JupyterHub installation.
- TensorFlow Model Analysis (TFMA) and TensorFlow Transform (TFT).
- Kubeflow Pipelines.
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Setting up Kubeflow in Google Cloud
There are three ways to set up a Google Cloud AI Platform Pipeline. This section will cover the steps for setting up a Cloud AL pipeline with full access to Google Cloud. You can create and deploy a pipeline with granular access or on an existing GKE cluster.
Following are the steps to set up Kubeflow in Google Cloud:
Step 1: Open AI Platform Pipelines in the Google Cloud console.
Step 2: Click New instance in the AI Platform Pipelines toolbar. This will open Kubeflow pipelines in Google Cloud Marketplace.
Step 3: Click Configure; the Deploy Kubeflow Pipelines form will open.
Step 4: If you see the Create a new cluster link, click on it or select a previous cluster from the drop-down menu.
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Step 5: Now, you must select the Cluster zone where you want your cluster to be located.
Step 6: Check the Allow access to the following Cloud APIs box to grant applications access to Google Cloud resources.
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Step 7: Click on Create Cluster. It’ll take several minutes before your cluster will be created.
Step 8: You’ll need namespaces to manage resources. You can either select default Namespaces or use custom.
Step 9: Now, enter a name in the app instance name box for the Kubeflow pipeline instance.
Step 10: With Managed storage, you can store your pipeline’s metadata and artifacts with cloud SQL in cloud storage rather than storing this information on compute engine persistent disk. To deploy your pipeline with Manage storage, select Use managed storage and provide the following information:
- Artifact storage Cloud Storage bucket.
- Cloud SQL instance connection name.
- Database username.
- Database password.
- Database name prefix.
Step 11: Click Deploy. You can access the pipeline’s dashboard from Google Cloud Console.
Conclusion
In this blog, you learn how Google pipelines streamline ML models’ development and help install repeatable and robust ML pipelines. You also saw the steps to build an end-to-end ML pipeline. Google guarantees an enterprise-ready and safe execution environment for ML workflows. Google Pipelines help teams to collaborate and quickly deploy machine learning workflows and bring their ML workflow into production, which can seem like a very complex task.
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