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The central repository for your ML Models provides extensive features to manage the lifecycle of your models – from versioning, experiment tracking, deploying to monitoring your models.
Get the whole picture of your data team's work with views of active trains, experiments, and deployments all in one place. When you need to find a model, search Layer's managed and centralized storage space for ML Models. It ensures that model artifacts are versioned and immutable which allows data teams to manage and monitor the lifecycle of ML Models at scale.
The status quo for data teams is that most analytics work is done only on a local machine, with only the outputs shared. With Layer, know who developed ML model(s), where the training datasets is, the libraries that have been used, what the environment is, what the KPIs are and more.
Re-discovering assets, re-prepping data, and manually deploying models distracts from modeling and prediction. Layer's declarative approach allows your team to define what to accomplish, rather than describe how to accomplish it so that your team can focus solely on producing insights and boosting their productivity.
With Layer, use the same data to reproduce a model, get the training dataset (protected if in production) model, parameters, random seeders, results, and more -- so that your team can pick up where another Data Scientist left off and continue producing, or repeating the same, knowledge.
Layer's Model Catalog is deeply integrated with the Data Catalog which enables end to end data lineage for every stage of your ML Models. See exactly what features were used to train your models, the parameters used and metrics logged.
It can take over 2 months for new hires to onboard onto new projects. There is no central place to find high quality features, nor examples of models or similar insights. With Layer, it's super easy to get familiar with existing projects by browsing and learning from company artifacts.
Centralized location for AI management across the enterprise, as well as a way to identify any duplication of efforts and opportunities for cost savings
Easy way to access performance
and audit logs for compliance purposes
Role-based access controls and better approach for managing AI security