Overview

Overview

Dataloop's Models module enables you to manage your model's lifecycle:

• Use public model architectures or connect your own models
• Train model versions with data items and their annotations
• Experiment with different hyper-parameters
• Evaluate your models by inferencing over datasets
• Compare model training and evaluation metrics, review false-positive and false-negative examples
• Deploy your models to run as a service with Dataloop applications
• Build continuous learning pipelines.

To access Models module: Select Models in the left-side navigation menu.

Model Creation Flow - Automated Status Assignment

Automated Status Assignment is a mechanism that intelligently applies a status label to a model depending on its current state, such as Created, Pre-trained, Trained, or Deployed. This status reflects the model's readiness for use or further development, based on the presence of certain artifacts or milestones achieved in the model's lifecycle.

Model Creation

When creating a new model, the platform now automatically assigns a status based on the presence of artifacts.

• If artifacts exist, indicating that the model has undergone some form of pre-training, it will be assigned the status Pre-trained.
• If no artifacts are present, the model will be given the status Created.

Model Cloning

• Created: Cloning a model with the status Created will result in a new model also with the status Created. This indicates that the model is in its initial stage without any training.
• Trained: When a model marked as Trained is cloned, the new model will inherit the status Pre-Trained. This reflects that the model has undergone training and possesses learned weights that make it ready for further fine-tuning or deployment.
• Deployed: Cloning a model with the status Deployed similarly results in a new model with the status Pre-Trained. This acknowledges that the model not only has been trained but also successfully deployed, indicating a level of robustness and reliability.
• Pre-Trained: Cloning a model already marked as Pre-trained maintains the status Pre-trained for the new model. This consistency underscores the model's readiness and the transferability of its pre-trained state.