Many organizations are migrating their data warehouses to Databricks, eager to take advantage of the tools it offers to build, train and deploy machine learning models at scale.

But to fully reap the benefits of AI/ML models, you need to be sure you can trust the data that you use to train them. Otherwise, you risk inaccurate and unreliable AI outputs. Watch this webinar to learn data validation best practices you should be implementing before you build AI/ML models on Databricks that will minimize the risk of inaccurate or unreliable outputs, including:

• Perform data validation and reconciliation checks before training or validating your AI models
• Automate data integrity testing to keep pace with the speed and scale of modern data pipelines and AI/ML engines
• Integrate continuous data integrity checks into your DataOps processes

Speakers:

• Curtis O’Dell, Business Performance Director, Data Integrity
• Andrew Prueser, Solution Architect- Presales