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What is Layer Model Catalog?

Layer Model Catalog provides a centralized, managed, indexed storage space for ML models. It ensures that model artifacts are versioned and immutable. This allows data teams to manage and monitor the lifecycle of the ML Models at scale.

What does Layer Model Catalog Do?#

  • Training At Scale: You can leverage Layer Data Catalog to reuse high quality training data from Featuresets and Datasets to train your models at scale.
  • Auto Versioning: An intuitive ML model versioning feature that allows for faster experimentation of your models.
  • Model Testing: Develop your unit, behavioral or back tests for automated model testing.
  • Performance Monitoring: Powerful observability on your ML models throughout their lifecycles. Track your parameters or advanced metrics or develop your own business KPI's to attribute how your model impacts your business.
  • Hyperparameter Tuning: Perfect your models through parallelized, easy-to-use hyper-parameter tuning. Click to learn more about Hyperparameter Tuning
  • Deep Integration: Layer can deploy your trained models to your model hosting solution whether it's AWS SageMaker or a Kubernetes cluster.

Benefits of Layer Model Catalog#

  • Faster experimentation
  • Reproducible pipelines
  • Powerful observability
  • Fast and reliable productionization