It’s a challenge familiar to many data-driven organizations: how to make data insights available to the business in a way that drives real value. Data applications can present machine learning (ML) insights to business users in the terms and metrics they use every day, but data scientists have not had a way to build and deliver those apps easily. As a result, far too many ML-based models have not been fully consumed by the business because they are not accessible to less-technical audiences.
In this ebook, you’ll learn how data scientists can now drive better value for the business by building data applications in their preferred language—Python. By using Snowflake to build and deploy ML models at scale and Streamlit to build interactive apps in Python, you can bridge the gap that’s prevented your organizations from fully transforming into a data-first company.
Read on to find out how you can:
- Use Snowflake to build and deploy ML models at scale
- Use Streamlit to build interactive apps in Python
- Leverage Streamlit-built apps to securely iterate with business teams to ensure your ML-derived insights deliver maximum value