Legacy Warning
Content in this section reflects outdated practices or deprecated features. It's recommended to avoid using these in new developments.
While existing implementations using these features will continue to receive support, we strongly advise adopting the latest standards and tools for new projects to ensure optimal performance and compatibility. For more information and up-to-date practices, please refer to our newest documentation at docs.kolena.io.
Core Concepts#
In this section, we'll get acquainted with the core concepts on Kolena, and learn in-depth about the various features
offered. For a brief introduction, see the Quickstart Guide or the
Building a Workflow tutorial. For code-level API documentation, see the
API Reference Documentation for the kolena
Python client.
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Testing in Kolena is broken down by the type of ML problem you're solving, called a workflow. Any ML problem that can be tested can be modeled as a workflow in Kolena.
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Test cases and test suites are used to organize test data in Kolena.
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In Kolena, a model is a deterministic transformation from test samples to inferences.