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. Register for 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:
Speakers:
The product featured in this webinar is Tricentis Data Integrity. Watch a 5-min overview and request a demo to learn more.