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πŸ’« About Me:

πŸš€ Leading the development of cloud-based applications for enterprise clients using AWS and various programming languages.

🀝 Seeking collaboration on projects related to building and scaling cloud-based applications for enterprise clients.

πŸ’ͺ Looking for assistance with staying current on cloud technologies and best practices, as well as improving skills in distributed systems design and architecture.

πŸ“š Learning new programming languages and technologies for use in the cloud, including Rust, Golang and Kotlin.

πŸ’» Experienced with AWS and cloud-based application development for enterprise clients, and happy to answer questions about these topics.

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πŸ’» Tech Stack:

C# GraphQL Go HTML5 JavaScript PHP Python TypeScript Shell Script AWS Cloudflare Datadog Firebase DigitalOcean Google Cloud Heroku Netlify Django DjangoREST Express.js FastAPI NodeJS Next JS NestJS NPM JWT Fastify Socket.io Vue.js Jenkins Nginx Apache MUI Styled Components jQuery Gatsby Bootstrap React Router Yarn AmazonDynamoDB MongoDB MySQL Postgres SQLite Canva NumPy Pandas Terraform Trello Vagrant Portfolio Confluence Ansible Docker ESLint ElasticSearch Jira Kubernetes Swagger Postman Notion Figma

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Peterson Oaikhenah's Projects

cors icon cors

Node.js CORS middleware

deedy-resume icon deedy-resume

A one page , two asymmetric column resume template in XeTeX that caters to an undergraduate Computer Science student

demos icon demos

Demo applications showing off Web Bluetooth

devops-master-class icon devops-master-class

Devops Tutorial for Beginners - Learn Docker, Kubernetes, Terraform, Ansible, Jenkins and Azure Devops

devops-repo icon devops-repo

Devops Automation Code for LFS261 Continuous Delivery Course

devops_project-ml-microservice-kubernetes icon devops_project-ml-microservice-kubernetes

oject Overview In this project, you will apply the skills you have acquired in this course to operationalize a Machine Learning Microservice API. You are given a pre-trained, sklearn model that has been trained to predict housing prices in Boston according to several features, such as average rooms in a home and data about highway access, teacher-to-pupil ratios, and so on. You can read more about the data, which was initially taken from Kaggle, on the data source site. This project tests your ability to operationalize a Python flask appβ€”in a provided file, app.pyβ€”that serves out predictions (inference) about housing prices through API calls. This project could be extended to any pre-trained machine learning model, such as those for image recognition and data labeling. Housing price prediction. Project Tasks Your project goal is to operationalize this working, machine learning microservice using kubernetes, which is an open-source system for automating the management of containerized applications. In this project you will: Test your project code using linting Complete a Dockerfile to containerize this application Deploy your containerized application using Docker and make a prediction Improve the log statements in the source code for this application Configure Kubernetes and create a Kubernetes cluster Deploy a container using Kubernetes and make a prediction Upload a complete Github repo with CircleCI to indicate that your code has been tested

django-api icon django-api

A simple API to allow admin users to view and edit the users and groups in the system.

dna_backend icon dna_backend

Manage all your loans with a personal loan tracker.

docker-mern-nginx icon docker-mern-nginx

MERN stack application with Docker and Nginx: Docker Compose for React Nodejs Express and MongoDB

e-health icon e-health

Appointment booking for patients and doctors.

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