Documentation > Data > Upload Dataset

Uploading Datasets

Datasets are the foundation of your machine learning models. Our platform makes it easy to upload, organize, and prepare your data for training - all without writing code.

Supported Formats

Currently, our platform supports uploading data in CSV (Comma-Separated Values) format. This widely-used format allows for structured tabular data representation.

Dataset Requirements

  • •File must be in CSV format (.csv extension)
  • •First row should contain column headers
  • •Maximum file size: 500MB
  • •Data should be clean and properly formatted

How to Upload Datasets

Our platform offers multiple ways to upload your datasets, making it convenient regardless of your workflow.

1

Drag and Drop

  1. Navigate to the Datasets page
  2. Drag your CSV file and drop it onto the datasets area
  3. The upload will begin automatically

Quickest method for a seamless workflow

2

Using the Upload Button

  1. Navigate to the Datasets page
  2. Click on the "New Dataset" card
  3. Select your CSV file in the file browser
  4. The upload will begin after selection

Understanding the Upload Process

When you upload a dataset, our platform processes it in several stages to prepare it for machine learning tasks.

1

File Upload

The file is securely transferred from your device to our servers with progress tracking.

2

Data Processing

The platform validates the CSV format, analyzes column types, and performs data quality checks.

3

Dataset Creation

After processing, the dataset is saved to your workspace and becomes available for use.

Upload Progress Tracking

During upload, a popup will appear showing:

  • •File name and size
  • •Upload progress percentage
  • •Processing status
  • •Option to cancel upload

Troubleshooting Dataset Uploads

If you encounter issues while uploading datasets, try these common solutions:

IssueSolution
Upload fails immediatelyCheck that your file is in CSV format and under the size limit.
Upload starts but never completesCheck your internet connection and try refreshing the page.
Error message about data formatEnsure your CSV is properly formatted with consistent delimiters and column structure.
Processing error after uploadCheck for missing values, non-standard characters, or inconsistent data types in your CSV.

Dataset Best Practices

To get the most out of our platform and ensure successful model training, follow these best practices:

Data Preparation

  • Clean your data before uploading (remove duplicates, fix errors)
  • Use clear, descriptive column names without spaces
  • Ensure consistent formatting of dates, numbers, and text
  • Handle missing values before uploading when possible

Dataset Organization

  • Use descriptive file names for your datasets
  • Consider creating separate datasets for training and validation
  • Document your dataset contents and sources
  • Regularly check for dataset updates if your source data changes

Next Steps After Upload

Once your dataset is successfully uploaded, you're ready to move forward with your machine learning project:

1

Explore your data

Use our visualization tools to understand distributions and correlations

2

Preprocess your data

Apply transformations and handle missing values

3

Select features

Choose which columns to use for training your model

4

Train a model

Select a model type and configure parameters

5

Evaluate performance

Assess your model's accuracy and other metrics

6

Deploy your model

Make your model available for predictions

Explore Your DataSkip to Model Training

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Last updated: 3/22/2025

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