Working with Data Scientists for Deployment Success
Best practices for deploying enterprise data science projects
We know how hard IT leaders work to keep the life cycle of their systems and models running efficiently, but do Data Science teams have the tools needed to successfully get models into production?
Join Open Data Group as we tackle some common Data Science concerns and show you operational best practices to ensure your analytic execution environment is the same across the creation, validation, and production stages.
This third installment of a 6-part series will specifically answer:
- Have multiple models and data sources? Learn the importance of enforcing a data schema system on your models; access, discover context, extract, and secure data
- Want more “business impact” from big data? Examine the relationship between data and project goals
- Trouble replicating model test results in a live production environment? Create data science confidence for model deployment success
- Concerned containerized adoption might slow your existing pipeline? Get clarification needed between data scientist and IT to locate and solve deployment problems with FastScore