Organizations are increasingly realising they need to consolidate the data input, pipelines and outputs of their data science work into a central place for both governance and reuse. This session will cover how a Data Warehouse can be used for both feature engineering and as a shared feature-repository for Data Science teams. We will show how capabilities of Snowflakes Data Warehouse service lend themselves to this use.
Top rated books
- The Book of Why: The New Science of Cause and Effect
- Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World
- Human + Machine: Reimagining Work in the Age of AI
- The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
- AI Crash Course: A fun and hands-on introduction to machine learning, reinforcement learning, deep learning, and artificial intelligence with Python
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Collaboratively harness market-driven processes whereas resource-leveling internal or "organic" sources.