Save Time, Increase Productivity and Improve Model Governance with H2O

This is an online event, register here: https://bit.ly/2WkpmNd

For many organisations, including Financial services and Healthcare companies, model documentation is a requirement for any Machine Learning model to be used within a production. For others, model documentation is part of a data science team’s best practices.

Model documentation includes how a model was created, training and test data characteristics, what alternatives were considered, how the model was evaluated, and information on model performance. Collecting and documenting this information can take a data scientist days to complete for each model. The model document needs to be comprehensive and consistent across various projects. The process of creating this documentation is tedious for the data scientist and wasteful for the business because the data scientist could be using that time to build additional models and create more value. Inconsistent or inaccurate model documentation can be an issue for model validation, governance, and regulatory compliance.

Join us on Tuesday, July 21st at 4pm CET, to learn how to automate the creation of comprehensive, high-quality model documentation that saves time, increases productivity, and improves model governance.

We are looking forward to having you join us!