AI model development relies on a robust foundation of data and data pipelines that ensure data is securely accessed, processed and served to data-hungry machine learning (ML) models. And to maintain responsible, high-quality models in production, successful experiments need to be monitored and model and data lineage needs to be tracked for regulatory compliance. All of this needs to be done in a way that scales from tens to hundreds of models in order to get the highest return on your AI investments.
Join ML experts from Snowflake and Microsoft to:
- Break down Snowflake platform components, including Snowpark, to help you think about your data and feature pipeline architecture
- Get deep insight into updates on integrations that streamline how you bring features into your models using Azure Machine Learning
- Learn how to use the joint solution through a walk through demo
Shankar Narayanan SGS
Principal Cloud Solution Architect,
Sr. Partner Sales Engineer Data Science,