Acceleratinag Data Science and Machine Learning with Snowflake DataTech
summary
Snowflake runs on AWS Azure and GCP and claims to handle at-scale workloads as data grows toward the cited ~168 zettabytes by 2025. The stack combines Snowpark multi-language UDFs with Anaconda-managed Python packages, Jupyter notebooks, scikit‑learn pipelines (grid search CV) and Streamlit front ends. The demo trains a model inside Snowflake converts the saved model to a model as UDF for in-database inference and shows queries and joins running without moving data; Marketplace supplies third-party datasets for enrichment.
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chapters (8)
- 0:00Introduction to Snowflake
- 3:17AI and ML evolution
- 5:05Breaking technical silos
- 7:11Snowflake’s integrated ecosystem
- 9:38End-to-end ML with Snowpark
- 15:44Building apps on data
- 17:52Live Python demo
- 21:44Streamlit applications
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