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Craig Wiley

Speaker

Craig Wiley

2

talks

AI/ML Product Mgmt Databricks

Craig Wiley leads Product for Artificial Intelligence at Databricks. He previously built and led Cloud AI Products at Google Cloud, including Vertex AI, and spent nine years at Amazon as the founding General Manager of AWS SageMaker and in pricing and analytics for Fulfillment By Amazon. He emphasizes democratizing data and balancing advanced tooling with accessibility for newer users. In his personal time, he enjoys time with family and biking.

Bio from: Data + AI Summit 2025

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Intro to the Mosaic AI Platform: Building Data Intelligence Into Your AI Solutions

Take a front-row seat for a comprehensive, high-level introduction to Mosaic AI through the lens of Data Intelligence. In this session, we’ll spotlight the Databricks Platform’s newest features and announcements, showcase how Mosaic AI transforms raw enterprise data into actionable insights and share real-world examples of success. Whether you’re beginning your AI journey or scaling your existing efforts, this talk will provide you with the foundational knowledge and inspiration to fully leverage Mosaic AI for Data Intelligence and next-generation GenAI solutions.

Traditional ML at Scale: Implementing Classical Techniques With Databricks Mosaic AI

Struggling to implement traditional machine learning models that deliver real business value? Join us for a hands-on exploration of classical ML techniques powered by Databricks' Mosaic AI platform. This session focuses on time-tested approaches like regression, classification and clustering — showing how these foundational methods can solve real business problems when combined with Databricks' scalable infrastructure and MLOps capabilities. Key takeaways: Building production-ready ML pipelines for common business use cases including customer segmentation, demand forecasting and anomaly detection Optimizing model performance using Databricks' distributed computing capabilities for large-scale datasets Implementing automated feature engineering and selection workflows Establishing robust MLOps practices for model monitoring, retraining and governance Integrating classical ML models with modern data processing techniques