> ## Content Index
> Fetch the complete content index at: https://blog.unitlab.ai/llms.txt
> Use this file to discover other available public pages before exploring further.

# Dataset Management at Unitlab AI [2026]
- URL: https://blog.unitlab.ai/unitlab-ai-dataset-management/
- Published: 2024-10-23T10:10:13.000Z
- Updated: 2026-08-24T12:58:58.000Z
- Description: A comprehensive guide to manage and release AI/ML datasets with Unitlab AI. Updated for 2026.
- Author: Hojiakbar Barotov
- Tags: Data, Dataset Management, Unitlab AI, Data Annotation

## Current Unitlab platform

This article preserves its original educational or historical topic. Unitlab AI is now an enterprise multimodal data platform for curating, annotating, managing, versioning, reviewing, and preparing image, video, audio, text, document, medical, pathology, and geospatial training data. Interface screenshots below may reflect the product version available when the article was published.

![Current Unitlab AI multimodal data annotation platform](https://storage.ghost.io/c/48/f4/48f4b614-5c29-430d-9cb3-e0b3f34395f3/content/images/2026/08/unitlab-current-multimodal-platform.webp)

Current Unitlab AI multimodal annotation workspace.

[Explore the current multimodal platform](https://unitlab.ai/en/multimodal-annotation?ref=blog.unitlab.ai) or follow the [end-to-end quickstart](https://docs.unitlab.ai/documentation/get-started/end-to-end-quickstart?ref=blog.unitlab.ai).

*Note: This is a simplified, quick version of our project creation docs. For full information, refer to this page:*

[Manage Datasets | DocumentationDataset Management and Version Control![](https://storage.ghost.io/c/48/f4/48f4b614-5c29-430d-9cb3-e0b3f34395f3/content/images/icon/spaces-2FVY6yJqW9wWHP6u0XYu5Q-2Ficon-2Fi1vaXGc3tUkwBAUdmVKL-2Flogo-2.png)Documentation![](https://storage.ghost.io/c/48/f4/48f4b614-5c29-430d-9cb3-e0b3f34395f3/content/images/thumbnail/spaces-2FVY6yJqW9wWHP6u0XYu5Q-2Fsocialpreview-2FcNv0xnqHoiNMlwoAjlcJ-2Fspaces_VY6yJqW9wWHP6u0XYu5Q_uploads_Xio36rhvnNnmK5WYwPak_Introduction-2.webp)](https://docs.unitlab.ai/dataset-management/manage-datasets?ref=blog.unitlab.ai)

Dataset Management Docs | Unitlab AI

Efficient dataset management is vital for building, training, and validating AI and Machine Learning (ML) models. Datasets in machine learning, essentially, are a collection of labeled data ready for training AI/ML models. Datasets can consist of different types of data, images, videos, text, audio, or a combination of those.

Dataset management refers to the process of creating and managing these source data for training and testing. Because training, testing, and validating AI/ML models is an iterative process, effectively managing different versions of our source data is a fundamental task.

Organizations rely on this process to ensure data quality, compliance, and business value.

In this post, we learn how to release and manage a dataset in Unitlab AI.

## Dataset Management in Unitlab AI

Unitlab AI simplifies dataset management with centralized version control, similar to [git](https://en.wikipedia.org/wiki/Git?ref=blog.unitlab.ai) commits. This allows ML datasets to evolve over time while staying consistent, relevant, and organized. We can revert back to any version if needs be.

ML models are inherently iterative: Data annotators release specific versions for different uses, update them as additional source data are added, and keep track of all changes, ensuring smooth versioning and reproducibility. Our platform also assigns role-based access permissions to datasets to maintain data integrity and safety.

Metadata, attributes, and variables are defined for each file, and files can be stored in various formats, such as CSV, XML, or JSON, to facilitate data analysis and statistical analysis. The structure of datasets, whether in hierarchical formats like JSON, XML, or YAML, supports different analytical needs and use cases.

![Public Dataset Viewer | Unitlab AI](https://storage.ghost.io/c/48/f4/48f4b614-5c29-430d-9cb3-e0b3f34395f3/content/images/2026/01/i1-1.png)

Public Dataset Viewer | Unitlab AI

Dataset release involves creating a new updated version in the iterative pipeline of ML development. As the dataset grows, newer versions can be released to reflect changes and additions. Note that adequate data storage solutions, such as data lakes, are essential as well to handle increasing storage capacity requirements as datasets scale.

Version control ensures that every change is tracked, with options to revert to earlier versions or compare updates. This means no manual dataset naming and management. With Unitlab AI, you no longer need to name dataset files as *Dataset\_final\_V10\_THIS\_ONE*. Everything will be stored and managed on the cloud by the platform.

![Unitlab AI fixes this dataset madness; Use it](https://storage.ghost.io/c/48/f4/48f4b614-5c29-430d-9cb3-e0b3f34395f3/content/images/2026/01/i0-2.jpeg)

Unitlab AI fixes this dataset madness; Use it

[Try it now](https://app.unitlab.ai/register?ref=blog.unitlab.ai)

In the second half of the tutorial, we will see how exactly Unitlab AI handles dataset management, release management, and [dataset versioning](https://blog.unitlab.ai/dataset-version-control/).

## Dataset Management

We create a data annotation project, annotate data, and release a dataset.

### Project Setup

To release an initial version of our ML dataset, we set up a project within Unitlab AI and annotate the source, raw data. The initial data files you upload represent raw data; these are the original, unprocessed images, text, or information that will be transformed into a labeled training set.

For example, you might upload a folder of unlabeled images as your starting point. The annotated images will then form the training datasets used for machine learning models.

In our [previous](https://blog.unitlab.ai/unitlab-ai-project-setup/) post, we learned how to set up a project at Unitlab AI. If you do not have a project, check out our guide and set up a project. We will continue with the project we created in the last post.

[Creating Your First Project at Unitlab AI PlatformA tutorial to create a data labeling project at Unitlab AI.![](https://storage.ghost.io/c/48/f4/48f4b614-5c29-430d-9cb3-e0b3f34395f3/content/images/icon/652fe0711ca7a678ec836f1b_FavIcon-260.png)Unitlab BlogsHojiakbar Barotov![](https://storage.ghost.io/c/48/f4/48f4b614-5c29-430d-9cb3-e0b3f34395f3/content/images/thumbnail/Project-7.png)](https://blog.unitlab.ai/unitlab-ai-project-setup/)

Project Creation at Unitlab AI

## Data Labeling

To release a dataset, we first label our source data. If an image is not labeled, then it will not be included in the dataset release. The difference between a project and dataset is that the project contains all images, labeled or not, whereas the released dataset contains only the labeled images with specific values assigned during image annotation.

💡

Unitlab AI provides [foundational AI models](https://docs.unitlab.ai/auto-labeling/segment-anything-sam?ref=blog.unitlab.ai) to accelerate annotation!

Various types of data can be annotated, including unstructured data such as images, videos, and text documents. For instance, for text-based unstructured data, natural language processing techniques can be used to extract and label relevant information.

On our platform, various built-in or [integrated AI models](https://docs.unitlab.ai/ai-models/model-integration?ref=blog.unitlab.ai) can be used to automatically annotate source data. Alternatively, data annotators can label images themselves. In our case, we have selected *Fashion Segmentation* and *Person Detection* as our AI model to auto-annotate our source images.

Auto-annotation using AI has several benefits: AI-powered models speed up the image labeling process up to 15 times. It also contributes to improved accuracy and model performance by ensuring consistent and reliable annotations. Finally, human annotators can adopt the [human-in-the-loop approach](https://blog.unitlab.ai/human-in-the-loop-in-data-annotation/) to produce high-quality datasets.

In the demo video below, we use *Crop Auto-Annotation* to annotate our image. You specify the area to label, and the [automation workflow](https://blog.unitlab.ai/unitlab-ai-automation-workflow/) currently in use will label that area, fast and accurate:

0:00 

/0:10 

1× 

Crop Auto-Annotation for Data Annotation

💡

The [Crop Auto-Annotation](https://docs.unitlab.ai/auto-labeling/crop-auto-annotation?ref=blog.unitlab.ai) is available to all users and is free forever.

This way, you can label images in a fast, efficient way. Annotate a few more images to release our dataset. In my case, I annotated a total of six images to release an initial version of our demo dataset:

![](https://storage.ghost.io/c/48/f4/48f4b614-5c29-430d-9cb3-e0b3f34395f3/content/images/2026/01/i2-1.png)

When we release this dataset, only these 6 images are included.

## Dataset Release

After annotating our project, we can release a dataset in the dashboard of our project. Because we are releasing the dataset for the first time, we do not have previous versions. The version automatically starts at 0.1\. We can now release a dataset, by clicking the `+ Release Dataset` button.

![Dataset Release | Unitlab AI](https://storage.ghost.io/c/48/f4/48f4b614-5c29-430d-9cb3-e0b3f34395f3/content/images/2026/01/i3-4.png)

Dataset Release | Unitlab AI

Since we are currently under the [Free plan](https://unitlab.ai/en/pricing?ref=blog.unitlab.ai), which is good for hobbyists and students, our released dataset will be public and available through Unitlab AI by default. If you want to create a private dataset (which you would want almost always, because your dataset is your strategic asset), you can [upgrade](https://unitlab.ai/en/pricing?ref=blog.unitlab.ai) to plans Active or above.

![Dataset Distribution, Format and License | Unitlab AI](https://storage.ghost.io/c/48/f4/48f4b614-5c29-430d-9cb3-e0b3f34395f3/content/images/2026/01/i4-1.png)

Dataset Distribution, Format and License | Unitlab AI

We see that the number of annotated images is 6, which matches our expectations.

We must choose the format for our dataset. We can choose from *COCO*, *YOLOv5* or *YOLOv8*; other types are coming in the future releases of Unitlab AI. We chose the general purpose *COCO*. However, it is possible to choose other formats depending on the business needs.

We also need to choose an open-source license. An open-source license grants certain permissions to the user that is using a particular piece of software or dataset. In essence, the license dictates what can and cannot be done with the dataset. There are, as of now, four licensing options available at Unitlab AI:

- [MIT](https://opensource.org/license/mit?ref=blog.unitlab.ai) \- Permissive, allows commercial use
- [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/deed.en?ref=blog.unitlab.ai) \- Requires attribution
- [Public Domain](https://en.wikipedia.org/wiki/Public%5Fdomain?ref=blog.unitlab.ai) \- No restrictions
- [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en?ref=blog.unitlab.ai) \- Restricts commercial use and requires the same license for derivatives

We chose MIT, an open-source license widely used for open-source projects, but you can select other licenses if you choose to open-source your dataset.

Finally, we specify the dataset distribution. In practice, you divide your ML dataset into training, validation, and testing to train your ML models. The common distribution is 80-10-10; but you can determine it yourself.

![Released Dataset #0.1](https://storage.ghost.io/c/48/f4/48f4b614-5c29-430d-9cb3-e0b3f34395f3/content/images/2026/01/i5-1.png)

Released Dataset #0.1

Our dataset by default takes on the name of the project and is accessible on the *Dashboard* of the Unitlab AI. The dashboard also provides search functionality, allowing you to quickly locate specific datasets or data points:

![Dataset Dashboard | Unitlab AI](https://storage.ghost.io/c/48/f4/48f4b614-5c29-430d-9cb3-e0b3f34395f3/content/images/2026/01/i6-3.png)

Dataset Dashboard | Unitlab AI

## Dataset Management

We can now manage our newly released dataset. When you click on the newly created dataset, we can carry out several management actions, such as cloning, augmenting, and downloading the dataset.

First, go to the `My Datasets` pane on the left and click on your newly released dataset:

![My Datasets Section | Unitlab AI](https://storage.ghost.io/c/48/f4/48f4b614-5c29-430d-9cb3-e0b3f34395f3/content/images/2026/01/i7-2.png)

My Datasets Section | Unitlab AI

### Dataset Clone and Version Control

In Unitlab AI, users can clone public datasets and start working with the dataset. Our platforms offers numerous public official and user-created [datasets](https://app.unitlab.ai/Content/datasets?ref=blog.unitlab.ai). You may clone a dataset for several reasons: testing, experimentation, etc.

You can clone a dataset as follows. First, click `Clone Dataset` and leave the checkbox on. This clones the exact dataset with annotations:

![](https://storage.ghost.io/c/48/f4/48f4b614-5c29-430d-9cb3-e0b3f34395f3/content/images/2026/01/i8-1.png)

Cloning Dataset | Unitlab AI

If we clone with the annotations, our project contains all the image annotations in addition to the source images. Otherwise, we only clone the source images. The rest will continue in the same way as creating a project.

When you finish cloning, you will see the same project with the same name, labeled images, and annotations. Only the members and roles may differ:

![Cloned Project | Unitlab AI](https://storage.ghost.io/c/48/f4/48f4b614-5c29-430d-9cb3-e0b3f34395f3/content/images/2026/01/i9-2.png)

Cloned Project | Unitlab AI

### Dataset Download

Our public dataset is now released and ready to be used. All Unitlab AI users can now clone it and use it for their own computer vision projects if necessary, under the *MIT* license.

If we have a need to download the source images or data annotations in our labeled dataset to our local storage, Unitlab AI enables us to download them in a programmatic ([CLI and Python SDK](https://docs.unitlab.ai/cli-python-sdk/get-started?ref=blog.unitlab.ai)) and non-programmatic (Web browser) way.

If we want to download the source images, we can download the images in a ***zip*** format. We can separately download the image annotations in a ***JSON*** format.

As the primary use case of annotation is for training and testing ML/AI models, the models expect the data annotations in a standardized format that they can understand.

![](https://storage.ghost.io/c/48/f4/48f4b614-5c29-430d-9cb3-e0b3f34395f3/content/images/2026/01/i10.png)

Downloading Dataset | Unitlab AI

We have three options to download the results:

1. Web interface: the standard browser functionality (which we will use).
2. Unitlab CLI: the command-line interface.
3. Python package: the `unitlab` Python package.

[How to Configure and Use Unitlab Python SDK GuideManage your projects and datasets efficiently with Python and Unitlab CLI! Updated for 2026.![](https://storage.ghost.io/c/48/f4/48f4b614-5c29-430d-9cb3-e0b3f34395f3/content/images/icon/652fe0711ca7a678ec836f1b_FavIcon-261.png)Unitlab BlogsHojiakbar Barotov![](https://storage.ghost.io/c/48/f4/48f4b614-5c29-430d-9cb3-e0b3f34395f3/content/images/thumbnail/SDK.png)](https://blog.unitlab.ai/configure-unitlab-cli-sdk/)

The CLI and Python SDK | Unitlab AI

The pop-up itself has also guides for using the CLI and SDK:

![Dataset Download by CLI | Unitlab AI](https://storage.ghost.io/c/48/f4/48f4b614-5c29-430d-9cb3-e0b3f34395f3/content/images/2026/01/i11-1.png)

Dataset Download by CLI | Unitlab AI

### Data Augmentation

If you go to the `Data` tab inside your dataset management, you can create a new synthetic version of your dataset by augmenting data. [Data augmentation](https://www.ibm.com/think/topics/data-augmentation?ref=blog.unitlab.ai) refers to the fact that we can use pre-existing data to create new data samples that can improve model optimization and generalizability. This way, we can create new versions of datasets.

![](https://storage.ghost.io/c/48/f4/48f4b614-5c29-430d-9cb3-e0b3f34395f3/content/images/2026/01/i12-1.png)

Data Augmentation Tab | Unitlab AI

Follow this article for an in-depth guide to data augmentation:

[Data Augmentation Techniques for Comaputer VisionMaster data augmentation in computer vision with this complete guide. Learn how data augmentation techniques vary and discover how to optimize sample data for your object detection model.![](https://storage.ghost.io/c/48/f4/48f4b614-5c29-430d-9cb3-e0b3f34395f3/content/images/icon/unitlab-6-3.png)Unitlab BlogsAsad Iqbal![](https://storage.ghost.io/c/48/f4/48f4b614-5c29-430d-9cb3-e0b3f34395f3/content/images/thumbnail/Robotics--3-.png)](https://blog.unitlab.ai/data-augmentation/)

Data Augmentation | Unitlab AI

Now, you can edit different configurations for augmentation, but we will proceed with default values and create 100x augmented images. However, because we are under the free plan, we can create only 5x augmented images:

![Data Augmentation 100x | Unitlab AI](https://storage.ghost.io/c/48/f4/48f4b614-5c29-430d-9cb3-e0b3f34395f3/content/images/2026/01/i13.png)

Data Augmentation 100x | Unitlab AI

Now, we have 36 images in total in our second version of our dataset, due to data augmentation. Pretty elegant, innit?

![Augmented Dataset | Unitlab AI](https://storage.ghost.io/c/48/f4/48f4b614-5c29-430d-9cb3-e0b3f34395f3/content/images/2026/01/i14.png)

Augmented Dataset | Unitlab AI

## Conclusion

Unitlab’s version-controlled dataset management ensures that data remains consistent and accessible throughout the project. Whether the goal is to build an AI/ML model, collaborate with a team, or share work publicly, the platform provides a smooth, reliable way to manage and release datasets.

With options for tracking changes, selecting licenses, cloning, data augmentation, and flexible download methods, Unitlab AI makes it easy to create structured, reproducible datasets for any purpose.

By keeping datasets organized and up to date, projects benefit from improved collaboration, reduced errors, and better AI performance.

[Explore Unitlab](https://app.unitlab.ai/register?ref=blog.unitlab.ai)

## Explore More

- [Workspace Management at Unitlab AI](https://blog.unitlab.ai/unitlab-ai-workspace-management/)
- [Project Creation at Unitlab AI](https://blog.unitlab.ai/unitlab-ai-project-setup/)
- [Unitlab CLI and Python SDK Configuration \[2026\]](https://blog.unitlab.ai/configure-unitlab-cli-sdk/)

---

**Explore related Unitlab capabilities:** [a data curation platform](https://unitlab.ai/en/data-curation?ref=blog.unitlab.ai) and [AI training-data annotation](https://unitlab.ai/en/data-annotation?ref=blog.unitlab.ai).