AWS-SageMaker-Snowflake-ML-Pipeline CoreyLeath-code · PARTIAL
The **AWS SageMaker + Snowflake ML Pipeline** is a fully production-grade, end-to-end machine learning workflow designed to ingest large-scale data from Snowflake, perform feature engineering with Apache Spark, and train, tune, and deploy models on AWS SageMaker—all orchestrated and versioned with CI/CD, Terraform, and Ansible.
github.com/CoreyLeath-code/AWS-SageMaker-Snowflake-ML-Pipeline · ★ 5 · Forks 1 · Size 171 KB
SUMMARY
Technologies 14
Scored 13
Observed 13
Practices 6
Evidence 15
Skips 1
COVERAGE
Analyzed 46 files · 205 commits · 0 API calls
TECHNOLOGIES & DEPTH
Dockerfile LANGUAGE Depth 70
1 files · PRODUCTION
Shell LANGUAGE Depth 70
1 files · PRODUCTION
JSON LANGUAGE Depth 70
1 files · PRODUCTION
Markdown LANGUAGE Depth 70
7 files · PRODUCTION
YAML LANGUAGE Depth 70
5 files · PRODUCTION
Python LANGUAGE Depth 70
23 files · PRODUCTION
SQL LANGUAGE Depth 70
1 files · PRODUCTION
pip BUILD_TOOL Depth 80
1 files · CONFIGURATION
AWS SDK CLOUD Depth 77
4 files · PRODUCTION
Flask FRAMEWORK Depth 43
1 files · PRODUCTION
NumPy LIBRARY Depth 25
1 files · TEST
pandas LIBRARY Depth 81
7 files · PRODUCTION, TEST
pytest TESTING Depth 22
1 files · TEST
python-dotenv LIBRARY Depth —
0 files · config only
PRACTICES
documentation · observedautomated_tests · observedcontinuous_integration · observedcontainerization · observedlinting · absentformatting · absent
ACTIVITY & OWNERSHIP
First commit 2025-06-05
Last commit 2026-09-28
Active months 7
Commits 205