Sample programs for Cloud Computing course at UT Austin
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Updated
Jun 3, 2024 - Python
Sample programs for Cloud Computing course at UT Austin
I have created 2 microservices, containerized them using docker and deployed them on Kubernetes cluster (GKE), using CI/CD and registry for containers. The Infrastructer as Code (IaC) approach is used for creating a k8s cluster.
An end-to-end ML model deployment pipeline on GCP: train in Cloud Shell, containerize with Docker, push to Artifact Registry, deploy on GKE, and build a basic frontend to interact through exposed endpoints. This showcases the benefits of containerized deployments, centralized image management, and automated orchestration using GCP tools.
Explore microservices CI/CD on GKE, mastering containerization, Kubernetes orchestration using Docker, GCP tools (Source Repository, Cloud Build, GKE), and Terraform. This repository serves as a hands-on resource for learning RESTful APIs, navigating GKE clusters with persistent volumes, offering practical insights for modern deployment.
Deploying Machine Learning service in Google Kubernetes Engine (GKE) & Istio (Service Mesh).
Developing Django Apps Using Cloud Code with Google Kubernetes Engine (GKE)
Example of github-action that automatically deploy flask server to Google Kubernetes Engine
An E-Commerce web application developed using Django REST API for Backend and React using Material UI framework for the Frontend
Repository of the classify patient microservice implementation for the covid 19 patient classification application
MLOps Implementing "Brain Computer Interface" on Kubernetes
A minimal template using KRM to deploy a FastAPI app.
Airport security system simulation based on microservices, utilizing Object Detection via Google Vision API to detect suspicious human faces detected on security footage. The microservices are (Docker-) containerized RESTful services which are able to communicate with each other over HTTP requests within an internal network.
Kubeflow for Poets: A Guide to Containerization of the Machine Learning Production Pipeline
Full-stack microservices deployment for Google Kubernetes Engine and Amazon Elastic Container Service
In this tutorial we explain how to get real time analytics of energy produced and consumed from two solar stations simulators using influxDB together with grafana hosted on the kubernetes engine of google
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