Machine Learning · MLOps · Cloud
Developing platform capabilities for model deployment, monitoring, data quality, metadata, traceability and production maintenance.
AT A GLANCE
CONTEXT & PROBLEM
Machine-learning work in production required repeatable capabilities across model deployment, monitoring, data quality, versioning and maintenance. Documentation, metadata and traceability also needed to remain connected to the engineering lifecycle.
The platform had to connect model information, user-facing metadata entry, cloud metadata services and CI automation without reducing MLOps to model training alone.
MY ROLE
I developed MLOps platform capabilities, implemented the OpenMetadata integration on GCP, built the Streamlit metadata interface and automated CI steps for code analysis and documentation.
Developed platform capabilities supporting the production model lifecycle.
Implemented the OpenMetadata integration on GCP to centralize documentation, technical metadata and traceability.
Built a Streamlit frontend with Databricks API authentication and field validation.
Automated CI steps for code analysis, documentation generation and publication to Confluence.
Worked with deployment, monitoring, data quality, versioning and production-maintenance concerns using GCP, Databricks, Docker and Git within the platform environment.
CONCEPTUAL MODEL LIFECYCLE
Conceptual and anonymized view; implementation details are intentionally omitted.
ENGINEERING APPROACH
The platform treated metadata, traceability, interfaces, versioning and CI as parts of the production ML lifecycle. The implementation connected model contracts and validated metadata entry to documentation and lifecycle processes.
Worked on platform capabilities spanning deployment, monitoring, data quality and production maintenance.
Connected OpenMetadata to the GCP environment to centralize documentation, technical metadata and traceability.
Built a controlled Streamlit input path with Databricks API authentication and field validation.
Used Jenkins-based CI to analyze code, generate documentation, publish to Confluence and support metadata centralization.
ENGINEERING CHALLENGES
Challenge → why it mattered → responseFOCUSED IMPLEMENTATION
OpenMetadata on GCP
Streamlit · Databricks · CI
SOLUTION
CORE TECHNOLOGY
EVIDENCE-BASED IMPACT
Recorded outcomes from the project scope.
Standardized platform concerns across the production model lifecycle.
Centralized documentation, technical metadata and traceability through OpenMetadata on GCP.
Automated code analysis, documentation generation and publication to Confluence.
Added authenticated, field-validated metadata entry through the Streamlit interface.