Beibei_Du_IDS721_Projet1

Cloud Continuous Delivery of Microservice (MLOps or Data Engineering Focused)

• Create a Microservice in Flask or Fast API
• Push source code to Github
• Configure Build System to Deploy changes
• Use IaC (Infrastructure as Code) to deploy code
• Use either AWS, Azure, GCP (recommended services include Google App Engine, AWS App Runner or Azure App Services)
• Containerization is optional, but recommended: Create a virtual environment

Mindmap

1. Find an interested csv online
2. Cleaned the dataset if needed
3. Take a few columns and manipualte some calculations using Rust
4. Microservices

Flask Framework

Examples of the app in app2.py

1. china is not in the dataset, thus it will return "The country is not in the dataset"
2. The following is to show the predicted 2020 average wage in the United States

Test out the desired outputs in test_app.py

Deployment on AWS

• This is a good practice to deploy it on the AWS App Runner

1. Creating the Services
2. Configuration/Deployment Processes

Reference

https://github.com/noahgift/gcp-hello-ml

https://github.com/nogibjj/rust-mlops-template

https://www.kaggle.com/datasets/kabhishm/countries-by-average-wage (Kaggle Dataset)