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Layer does not require you to learn new domain specific languages. You write the features in SQL and Python with little-to-no difference and create short and powerful YAML files to tell what you want. Layer takes a declarative, high-level approach to MLOps. You do not have to be drowned in low-level details of how, but can focus on what.
Create complex feature with a built-in robust data transformation service, including feature aggregations with sliding windows or your custom logic in native Python.
Feature engineering is often repeated on new projects as prepped data / features are difficult to discover and access. Layer provides a central place to find high-quality data and features for use in projects, which is instantly search- and filter-able.
Re-useable across teams and models
Layer enables teams to reuse battle-tested features for new models and prevents reinvention of the wheel with single sources of truth that can be shared across the organisation.
Many data teams suffer from using different data when training and serving features. This leads to drifts and inconsistencies, which cause hard-to-debug bugs and model performance degradations. Layer uses declared feature definitions to generate training datasets and to serve the same features in production, preventing pipeline issues and removing associated bugs.
Feature engineering is often repeated on new projects as prepped data / features are difficult to discover and access. There is no central place to find high quality data and features for use in projects. Layer enables teams to reuse battle-tested features for new models and prevents reinvention of wheel.
Layer keeps track of the dependency graph between your datasets, features, and ML Models. It keeps the pipelines up-to-date by triggering builds (scheduled, reactive or manual) whenever needed without any babysitting. It's so simple that your data team can forget it after setting it.
Layer’s automatically tracks your data characteristics to alert you to drifts in data or drops in data quality. It can be set to trigger rebuilds when data drops, retrains of models, and offer manual solutions lie removing features from the input set.
Layer keeps track of features diligently. This enables better experimentation, "time travel" through older data, and auditibility. Through fully reproducible pipelines, users can debug and explain models.
Layer provides a unified interface for defining features. Once defined, features can be served offline and online. Layer handles all of the intricacies of performance, caching, consistency with no extra effort or input from you.