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
Justin Warner developed a production-ready, end-to-end real-time machine learning inference workflow for taxi duration prediction, contributed to the Snowflake-Labs/sf-samples repository. He designed and implemented a Jupyter Notebook that integrates feature engineering, online and offline feature store management, and XGBoost model training using Python and SQL. The solution includes model persistence, deployment through Snowpark Container Services, and supports both Python and HTTP-based prediction interfaces with model registry logging. By enabling low-latency feature retrieval and flexible deployment, Justin’s work provides a reusable reference implementation that streamlines real-time ML pipelines and addresses the need for fast, reliable inference in production 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
329 people
Work 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