MarkovML Projects
Organize your models, experiments and evaluations using MarkovML Projects.
Before diving into experiments or evaluations, it's essential to specify the active Project in MarkovML. This guide covers creating a new project or referencing an existing one, a crucial first step in streamlining your ML workflows.
Models play a pivotal role in machine learning applications. With the Markov SDK, you can register different models organized into projects and track their performance across various datasets.
Thus, MarkovML empowers you to organize your models, experiments, and evaluations efficiently using projects. Explore the capabilities of MarkovML Projects to streamline your machine learning workflows.
How MarkovML Project Works
Every time you train your model and want to track experiments and evaluations, it's easy to lose track of its purpose. Projects help organize your model registry, experiments, and evaluations for specific tasks or actions.
For instance, you can create a Project for Sentiment Analysis, where you register various models and conduct experiments and evaluations. This way, you can easily access and manage them without searching through your MarkovML workspace.
Updated 6 months ago