Snowflake-Labs/sf-samples
Sep 2025 – Sep 2025
1 Month active
Languages Used
Jupyter NotebookPythonSQL
Technical Skills
API IntegrationData EngineeringFeature StoreMachine LearningModel DeploymentReal-time Inference
Justin Warner
PROFILE
Justin Warner
Developed an end-to-end real-time machine learning inference workflow for taxi duration prediction, contributing to the Snowflake-Labs/sf-samples repository. The solution integrated feature engineering, online and offline feature stores, and XGBoost model training within a Jupyter Notebook, leveraging Python and SQL for data processing and model development. Model persistence and deployment were achieved using Snowpark Container Services, enabling both Python and HTTP-based prediction interfaces. Model registry logging was incorporated to support observability and flexible deployment. This production-ready reference implementation demonstrated low-latency feature retrieval and provided a reusable foundation for accelerating real-time ML pipelines in data engineering environments.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
884
Activity Months1
Your Network
378 peopleWork History
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025: Delivered a production-ready, end-to-end real-time ML inference workflow using Snowflake Online Feature Table for taxi duration predictions. Implemented an end-to-end notebook and README with feature engineering, online/offline store integration, and XGBoost model training; enhanced with model persistence, deployment via Snowpark Container Services (SPCS), and HTTP-serving with model registry logging. This work demonstrates low-latency feature retrieval, supports Python and HTTP usage, and provides a reusable reference implementation that accelerates real-time ML pipelines and delivers tangible business value through faster, more reliable inferences.
2 Commits • 1 Features
Sep 1, 2025September 2025: Delivered a production-ready, end-to-end real-time ML inference workflow using Snowflake Online Feature Table for taxi duration predictions. Implemented an end-to-end notebook and README with feature engineering, online/offline store integration, and XGBoost model training; enhanced with model persistence, deployment via Snowpark Container Services (SPCS), and HTTP-serving with model registry logging. This work demonstrates low-latency feature retrieval, supports Python and HTTP usage, and provides a reusable reference implementation that accelerates real-time ML pipelines and delivers tangible business value through faster, more reliable inferences.
September 2025
Activity
Loading activity data...
Quality Metrics
Correctness95.0%
Maintainability80.0%
Architecture95.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPythonSQL
Technical Skills
API IntegrationData EngineeringFeature StoreMachine LearningModel DeploymentReal-time InferenceSnowflakeSnowparkXGBoost
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline