nikbearbrown/INFO_7390_Art_and_Science_of_Data
Dec 2024 – Dec 2024
1 Month active
Languages Used
DockerfilePythonShellYAML
Technical Skills
AWS (EC2)AirflowCI/CDData EngineeringDockerFastAPI
dhirthacker7neu
PROFILE
Dhirthacker7neu
Developed an end-to-end CI/CD deployment pipeline for a Generative AI application in the nikbearbrown/INFO_7390_Art_and_Science_of_Data repository. The work centered on automating Docker image builds and deployments using GitHub Actions, with secure SSH-based delivery to AWS EC2 instances. Environment variable management and post-deployment health checks were integrated to ensure reliable, repeatable releases with minimal manual intervention. The approach leveraged Python, Docker, and YAML to orchestrate the workflow, improving deployment consistency and observability. No major bugs were reported during the period, and the focus remained on streamlining release processes to enhance deployment reliability for data-driven machine learning applications.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
10,345
Activity Months1
Your Network
173 peopleSame Organization
@northeastern.edu155
Shared Repositories
18
Work History
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for nikbearbrown/INFO_7390_Art_and_Science_of_Data. Focus: End-to-end CI/CD deployment pipeline for a Generative AI application. Key deliverable: Docker configurations, GitHub Actions to build and push images, SSH deployment to an EC2 instance, environment variable setup, and post-deployment health verification. Major bugs fixed: None reported this month. Overall impact: Enabled repeatable, automated deployments with reduced manual intervention, improving release velocity and deployment reliability. Technologies/skills demonstrated: Docker, GitHub Actions, SSH-based deployment to EC2, environment variable management, and health checks.
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for nikbearbrown/INFO_7390_Art_and_Science_of_Data. Focus: End-to-end CI/CD deployment pipeline for a Generative AI application. Key deliverable: Docker configurations, GitHub Actions to build and push images, SSH deployment to an EC2 instance, environment variable setup, and post-deployment health verification. Major bugs fixed: None reported this month. Overall impact: Enabled repeatable, automated deployments with reduced manual intervention, improving release velocity and deployment reliability. Technologies/skills demonstrated: Docker, GitHub Actions, SSH-based deployment to EC2, environment variable management, and health checks.
December 2024
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage80.0%
Skills & Technologies
Programming Languages
DockerfilePythonShellYAML
Technical Skills
AWS (EC2)AirflowCI/CDData EngineeringDockerFastAPIGitHub ActionsMachine LearningOpenAI APIPineconePythonRetrieval-Augmented Generation (RAG)SnowflakeStreamlit
Repositories Contributed To
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