Dataset and Model Management
Problem:
On Monday, Jen's team trained Model A on Dataset B. On Tuesday, Dan's team reviewed and rejected Dataset B for use.
Jen and Dan have never met.
If this sounds familiar, you're not alone.
Many cross-functional teams will contribute to a single project before it hits production.
They don't always communicate well. They don't always know one another exists.
Dataset and Model Management helps you register your models and datasets against one another, and the teams, workflows, and policies they're bound to.
Manage a distributed team's dataset and model library. Map relationships. Automate triggers.
Manage your datasets and model library. Connect any data integration source for visiblity and event triggering. Discuss as a team - directly on the models themselves.
Model and Dataset Management makes it easier to understand who's working on what and ingest data streams from monitoring systems.
Check out.
Check out datasets from your Dataset Library.
Make sure that the Workflows you're using them in have safety policies that check out.
Check out how fast you're moving trained models from concept to production.
Just don't let your team check out on you.
Efficacy
Execute Data science workflows effectively. Recall fewer systems from prod. Deal with less unacceptable and unexpected behavior.
Velocity
Projects move quickly from concept to production with clear expectations and requirements, coordination, and high visibility.
Consistency
Follow predictable standards and best-practices that everyone agrees on.
Continuity
Known data failure modes are analyzed and small steps are constantly taken to prevent them.
Knowability
An automated papertrail of how your people, processes, and tools worked together.
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