Entrez Utility Integration
LANCER
Lancer :
Ideal for quick, focused engagements
Team: ≤ 2 resources
Duration: ≤ 3 weeks
Indicative Budget: ≤ $3,000 USD*
Use Cases: MVPs, prototypes, simple websites, bug-fix sprints
Indicative Pricing: ~$15–$25/hr per resource*
Growth :
Best suited for growing products or structured builds
Team: 2–5 resources
Duration: Up to 4 months
Indicative Budget: Up to $30,000 USD*
Use Cases: Feature-rich web apps, integrations, custom dashboards
Indicative Pricing: ~$15–$35/hr per resource*
Enterprise :
Tailored for high-scale, complex or long-term initiatives
Team: 5+ resources
Duration: More than 4 months
Indicative Budget: Starts from $30,000+ USD*
Use Cases: End-to-end platforms, SaaS products, compliance-heavy systems
Indicative Pricing: ~$25–$50/hr depending on roles*
(Development)
Lancer :
Ideal for quick, focused engagements
Team: ≤ 2 resources
Duration: ≤ 3 weeks
Indicative Budget: ≤ $3,000 USD*
Use Cases: MVPs, prototypes, simple websites, bug-fix sprints
Indicative Pricing: ~$15–$25/hr per resource*
Growth :
Best suited for growing products or structured builds
Team: 2–5 resources
Duration: Up to 4 months
Indicative Budget: Up to $30,000 USD*
Use Cases: Feature-rich web apps, integrations, custom dashboards
Indicative Pricing: ~$15–$35/hr per resource*
Enterprise :
Tailored for high-scale, complex or long-term initiatives
Team: 5+ resources
Duration: More than 4 months
Indicative Budget: Starts from $30,000+ USD*
Use Cases: End-to-end platforms, SaaS products, compliance-heavy systems
Indicative Pricing: ~$25–$50/hr depending on roles*
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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