Overview
- Modernized the client’s AI/ML workflow using Snowflake, Snowpark for Python, and Streamlit for app development.
- Migrated campaign and auction data into Snowflake, creating a centralized and scalable data source.
- Refactored ML algorithms from Pandas and Scikit-learn into native Snowpark functions for improved performance and scalability.
- Deployed an integrated Streamlit app within Snowflake for real-time insights, model results, and bid timing recommendations.
In today’s digital advertising landscape, precision and timing are everything. One global software and services provider—trusted by enterprises to unify and streamline multi-channel ad campaigns—wanted to push the limits of campaign efficiency.
Their objective: optimize bidding strategy using AI to win more ad auctions at the right moment and price, while maximizing return on ad spend.
Challenge
Ad auctions happen in milliseconds, and the ability to identify the ideal moment to bid—when user engagement is likely and competition is reasonable—can be the difference between wasted budget and high-performance ROI.
While the client already had a working Python-based application for analyzing historical ad performance and bid timing, it wasn’t built for real-time decision-making or scalable ML operations. To move from reactive insights to proactive optimization, they needed a more robust, cloud-native AI/ML workflow that could process massive volumes of data and adapt quickly to ever-changing campaign variables.
Solution
We partnered with the client to modernize their machine learning pipeline using Snowflake’s powerful data infrastructure, Snowpark for Python, and Streamlit for interactive app development.
Key Achievements
- Data Migration & Integration: The team efficiently ingested all underlying campaign and auction data into Snowflake, creating a single, centralized data source with seamless access via a secure web interface.
- AI/ML Optimization: The team refactored core ML algorithms, originally written in Pandas and Scikit-learn, into native Snowpark functions. This shift brought compute closer to the data, improving performance, maintainability, and governance.
- Interactive Visualization: The team deployed a fully integrated Streamlit application within Snowflake, enabling business users and analysts to access real-time insights, model results, and bid timing recommendations through a clean, intuitive interface.
Impact
The result was a fully optimized, end-to-end AI solution embedded in the client’s advertising workflow. Key outcomes included:
- Optimized Bid Timing: AI models now pinpoint the most cost-effective moments to bid, increasing the likelihood of winning auctions while lowering acquisition costs.
- Improved Campaign ROI: Smarter bidding translates into better ad placements, higher engagement rates, and more effective budget utilization.
- Scalable, Future-Ready Infrastructure: With Snowpark and Streamlit embedded in their Snowflake environment, the team is set up to scale ML workloads, iterate faster, and operationalize new models with ease.
The client valued the speed of delivery, smooth migration, and reliable results. More importantly, this project laid the groundwork for broader AI innovation—allowing them to stay agile and competitive in a fast-moving industry where milliseconds matter.
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