Overview

The Dataloop platform provides a comprehensive suite of tools for managing, training, deploying, and monitoring AI models in production workflows. Its model management capabilities are designed to seamlessly integrate with Dataloop's data-centric pipeline, allowing teams to focus on building high-performing machine learning (ML) solutions while reducing operational overhead.

What Can You Do With Models in Dataloop?

• Automate Labeling: Speed up data annotation by letting models make initial predictions that humans can refine.
• Train Models: Use your labeled data to train new models directly in Dataloop or connect external training tools.
• Use Pre-Trained Models: Get started quickly with ready-made models for common tasks like object detection or image classification.
• Deploy Models: Host and run your models on Dataloop to make predictions in real-time, or in bulk.
• Monitor Performance: Keep track of how your models are performing and retrain them when needed using new or updated data.

Use the Data Browser to perform the following actions with models:

• Generate Predictions: Send items to models to create predictions (e.g., pre-annotations or testing), enhancing productivity and efficiency.
• Organize with Subsets: Assign items to subsets like train, test, or validation directly from the dataset browser to streamline model training and evaluation.
• Smart Search: Effortlessly find items linked to specific datasets or subsets using advanced search features for better data management.

Manage your model lifecycle efficiently with Dataloop:

• Leverage public model architectures or integrate your custom models.
• Train model versions using data items and their annotations.
• Test and fine-tune models with different hyperparameters.
• Evaluate model performance by running inferences on datasets.
• Analyze and compare training and evaluation metrics, including false-positive and false-negative results.
• Deploy models as services within Dataloop applications.
• Create continuous learning pipelines for ongoing model improvement.

Access Installed Models

To view models, select the Models from the left-side menu.

Create Your Own Model by SDK

This process involves setting up a model adapter and publishing it as an application that can be used within your projects.

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Model Creation Flow

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.

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Model Installations

Dataloop allows the installations for AI/ML models by allowing them to be hosted and executed on:

• Dataloop's Managed Compute (internal infrastructure): The Models run on the Dataloop's Compute. Learn how to Install.

• External Compute Providers (e.g., OpenAI, Azure, GCP, IBM, NVIDIA) via API Service Integration: The Models run on external provider's compute, which requires secret credentials to complete the installation. Learn how to Install.

Edit Access Integrations

To set access integration for installed models, refer to Edit Access Integrations.

Add Model Versions

After the Model DPK installation, you can add another models for this DPK, based on the DPK model components.

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