In this tutorial, we’re going to use create-react-app to quickly spin up a new React project. Then create a Docker image, with the help of Nginx, to create a containerized version of our React app. Next, we’ll push our Docker image to Google Container Registry and manually deploy our image to a Cloud Run service. Finally, we’ll set up a GitHub Action workflow to build, dockerize, and deploy our app whenever we push to master. Google’s Cloud Run product allows for scalable containerized applications in a fully managed serverless environment. Deploying a React App to Google Cloud Run with GitHub Actions
this tutorial will offer a solution to connect your VMs to your local SDE like VSCode while setting up Github to your VM to allow visible management. Without further ado, let’s dig in!
Demythify Kubernetes with Hands-on Exercise! How to Deploy Kubernetes to Your GCP Cloud <Step-by-step Tutorial>
This post follows up from the post earlier on training a multi-label image classification model and covers how to run the trained model in a python environment.
This article is specifically targeted at people with little to no experience with GCP. How to Crack the Google Cloud Professional Data Engineer Exam in 1 Month (October 2020)
Detailed walkthrough of the steps I took to solve Build and Secure Networks in Google Cloud: Challenge Lab Skill Badge on Google Cloud Platform
A detailed walkthrough of the steps I took to solve Perform Foundational Infrastructure Tasks in Google Cloud: Challenge Lab Skill Badge on Google Cloud Platform
Reinforcing Google’s decision made about deprecating the Google Cloud Messaging on 11th April 2019, everyone is required to migrate from GCM to FCM to ensure smoother push notification to the subscribed users. And, the migration part requires seamless efforts and technical expertise to carry out. So, this blog will unfold all the terms pertaining to GCM to FCM migration
In this quick fix, I’ll show you how to use a single-write, multi-read persistent block storage(ie: Google Persistent Disk or Amazon Elastic Block Store Volume) to store data on multiple nodes in a Kubernetes cluster using NFS.
Detailed walkthrough of the steps I took to solve the Deploy to Kubernetes in Google Cloud: Challenge Lab Skill Badge on Google Cloud Platform
Prerequisites. Create a Redis Instance. Memorystore (for Redis) instance is required for our implementation to store the processed data after the cloud dataflow pipeline is executed. Upload the input file in the GCS bucket.
Exploring different ways of running Kubernetes on Google Cloud Platform (GCP). Kubernetes on GCP: Simplicity vs. Flexiblity
In this short article, I will introduce you how to use Google Cloud service (BigQuery + DataStudio Free plan) to explore the open-source dataset with example COVID-19 dataset from the Google Cloud Public Datasets Program.
Build Interactive Apps with Google Assistant: Challenge Lab Tutorial. Detailed walkthrough of the steps I took to solve Build Interactive Apps with Google Assistant: Challenge Lab Skill Badge on Google Cloud Platform
In his article "3 Risk-Mitigation Lessons That We Learned The Hard Way This Year" our colleague Shashank, set five objectives to mitigate our overall project risks.
In this section, the main areas are progressively more technical and requires hands-on experience to really understand how to deploy services such as GKE, DataStore, Pub/Sub, etc. Networking, Storage and Compute are covered in detail with a focus on Compute Engine, GKE, BigQuery, Cloud Storage, and Deployment Manager.
This post introduces some of the basic ideas of serverless computing, and helps you deploy a serverless app to GCP.
My superpower toolkit: TFRecorder, TensorFlow Cloud, AI Platform Predictions and Weights & Biases. Lightweight yet scalable TensorFlow workflow on Google Cloud
To set up a fully operational machine learning Server on Google Cloud Compute Engine’s virtual machine instance. Here’s a step-by-step approach on how to configure a fully functional R Studio Server on Google Cloud.
This post is meant to allow all those companies that are not flooded by VC capital or have budgets to hire 7 figure teams of engineers to look after their cloud estate (a reasonable/unreasonable argument can be made that if you can’t afford said teams, you’re better off going the “buy” route instead of “build” but I digress, that can be a topic for a different post).