Feat: Build open-source microservice architecture cloud environments to provide tailored analytics for distinct industry sectors.

[siddhant-khisty]

Description

Develop a machine learning model targeting a specific industry (e.g., healthcare, finance, retail, etc.) and operationalize its deployment within a Kubernetes-managed infrastructure. The task should cover the full lifecycle, from gathering and preprocessing relevant data for the chosen vertical to training and validating the machine learning model. Upon the completion of the model development phase, deploy the trained model to a Kubernetes cluster via Devtron, integrating sophisticated automation mechanisms such as Continuous Integration and Continuous Deployment (CI/CD) pipelines, facilitating the seamless orchestration and delivery of model updates and enhancements. The ultimate objective is to provide an advanced, scalable, and maintainable architecture for deploying, updating, and monitoring machine learning models in a distributed cloud-native environment.

Deliverable

• A fully functional application analyzing data for a specific industry (e.g., healthcare, finance, retail, etc.) and getting results through a web interface
• Containerize the application and build it in a Kubernetes-native environment
• Automate the model training by implementing CI/CD and deploy it to Kubernetes

Key Competencies

• Basic Programming
• Curious enough to learn and implement

Recommended Skills to have:

• Go/Python/R
• Postman
• Kubernetes Basics
• Containerization
• Some experience with development

Mentors

• Nishant Kumar (@nishant-d)
• Kartik Singhal (@kartik-579)
• Prakhar Katiyar (@prkhrkat)

Skill Level: Easy, Medium Time: ~350 hrs