---
title: "Models Overview"
slug: "models-overview"
updated: 2026-07-04T09:58:25Z
published: 2026-07-04T09:58:25Z
canonical: "docs.dataloop.ai/models-overview"
---

> ## Documentation Index
> Fetch the complete documentation index at: https://docs.dataloop.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Models Overview

## Overview

The DDOE 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 DDOE'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 DDOE?**

- **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 DDOE 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 DDOE 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**](https://docs.dataloop.ai/docs/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 DDOE**:

- 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 DDOE applications.
- Create continuous learning pipelines for ongoing model improvement.

To install, train and deploy models using DDOE SDK, [read this tutorial](https://developers.dataloop.ai/tutorials/model_management/) on our Developer portal site.

## Access Installed Models

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

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## 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.

[Learn more](https://docs.dataloop.ai/docs/dl-hub-models#create-your-own-model-by-sdk)

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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.

[Learn more](https://docs.dataloop.ai/docs/dl-hub-models#model-creation-flow)

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## Model Installations

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

- DDOE's **Managed Compute** (internal infrastructure): The Models run on the DDOE's Compute. [Learn how to Install](https://docs.dataloop.ai/docs/dl-hub-models#install-models-running-on-dataloop-computes).
- 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](https://docs.dataloop.ai/docs/dl-hub-models#install-models-running-on-external-computes).

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## Edit Access Integrations

To set access integration for installed models, refer to [Edit Access Integrations](https://docs.dataloop.ai/docs/dl-hub-models#edit-integration-credentials).

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## Add Model Versions

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

[Learn more](https://docs.dataloop.ai/docs/dl-hub-models#add-model-versions)