Technologies Involved:
CI/CD
Area Of Work: Machine Learning
Project Description

A research-focused organization advancing biomedical tools and data access sought a streamlined setup for their Python-based Entrez Utility application. They needed a reliable, scalable development environment and automated deployment pipeline to accelerate internal development cycles and reduce manual errors.

Scope Of Work

The project focused on reviewing and re-building a partially implemented CI/CD pipeline between GitHub and AWS. Key objectives included automating deployments, simplifying the development setup, and ensuring stability and consistency across environments for seamless releases.

Our Solution

Oodles Platform delivered a comprehensive guide and automation framework to optimize the Entrez Utility setup and deployment. This included:

  • Step-by-step Environment Setup: Detailed instructions for cloning the repository, creating and activating a Python 3.6 virtual environment, installing dependencies, and configuring PostgreSQL as the database.
  • Database Management: Integrated Django migrations to automate database schema updates and maintain consistency across environments.
  • Local Development Simplification: Enabled easy server startup with minimal manual steps, reducing onboarding time for developers.
  • CI/CD Pipeline Enhancement: Rebuilt the continuous integration and deployment process connecting GitHub commits to AWS, leveraging automation tools to streamline code releases.
  • Quality and Stability Focus: The pipeline included automated tests and code checks to catch errors early and prevent faulty deployments.
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