Project Detail

In-House Framework for Machine Learning Deployment

Extended and improved an internal machine learning deployment framework while helping application teams use it effectively across development and deployment workflows.

Role
Software Developer
Context
Telecommunications
Domain
ML Platforms
Duration
14 months
Status
Finished

What changed

  1. Extended the framework’s monitoring and alerting capabilities through YAML-based configuration, improving operational support for deployed applications.

  2. Worked closely with application teams to help developers deploy their applications and adapt them to framework requirements.

  3. Refactored framework components and implemented additional features to improve maintainability and day-to-day usability.

  4. Reviewed application code before deployment and taught internal teams how to use the framework effectively.

Technology

Python

8 technologies

TensorFlow scikit-learn pandas SQLAlchemy PyYAML mypy Black unittest

Docker

1 technology

Dockerfile

SQL

2 technologies

PL/SQL HiveQL

CI/CD Pipelines

2 technologies

Azure DevOps GitLab CI/CD

Signals

machine-learning mlops platform-engineering devops internal-tooling