Layer is a declarative MLOps platform to help data teams across all companies produce machine learning (ML) applications based on code. It orchestrates your data pipeline for you. Layer enables you to focus on designing, developing, and deploying models without worrying about the infrastructure.
Machine learning operations, or MLOps, is a set of tools, principles, and practices that guide machine learning development.
The process of developing models is generally:
- Prepare the data.
- Create a machine learning model.
- Deploy the model.
- Monitor the model
With declarative MLOps (DM), you define what to accomplish, rather than describe how to accomplish it. You provide the definitions of your input entities (such as datasets, features, and models), then the DM platform builds, tests, and versions the output entities for you.
Who uses Layer?
We built Layer to empower data teams. It can be used by:
- Data scientists to develop machine learning models and track experiments.
- ML/data engineers to streamline machine learning operations.
- Data/business analysts to develop a single source of truth and high-quality features.
Supported ML frameworks
Layer supports the following frameworks:
- scikit-learn (without XGBoost)