Label Studio 1.12.0 🚀Automate & Evaluate Labeling Predictions Using LLMs & ML Models

A Step-by-Step Guide to Preparing Data for Labeling

Label Studio Team

In the world of machine learning, data labeling plays a central role in training accurate and reliable models, and properly preparing your data for labeling sets the foundation for the success of your machine learning projects. This step-by-step guide will walk you through the process of ensuring your data is optimized for the best outcomes in your AI endeavors.

Define Your Objectives

To begin, it is essential to define your goals and objectives for your machine learning project. What problems do you want your model to solve? This step sets the direction for your entire project. Additionally, you need to determine the type of data you need – whether it's images, text, audio, or another data type.

Once you’ve determined what kinds of data you will be labeling, determining the classes for your machine learning model is crucial. Determining what you want your model to take away from your training dataset determines your model’s success, and it requires careful consideration. Here are some effective methods to help you identify the right labels:

  • Consult with domain experts: Engage with subject matter experts to gain insights into the key categories relevant to your project. Their expertise can provide valuable nuances that may not be obvious from the data alone.
  • Conduct a literature review: Take a closer look at academic papers, industry reports, and case studies to understand how similar problems have been approached and what classes have been used in those scenarios.
  • Analyze your dataset: Thoroughly analyze your dataset to identify natural groupings or patterns. Techniques like clustering can reveal inherent structures in the data that can guide your class selection. Using Label Studio Enterprise’s Data Discovery tool can also help here by providing vector-based natural language search and similarity search to zero in on what possibilities exist in your potential dataset.
  • Conduct pilot studies: Run small-scale experiments to test different class structures and determine which ones yield the most meaningful results for your objectives.
  • Apply feedback loops: As you develop your model, refine your classes iteratively based on feedback from the model's performance and stakeholder input.

Data Collection

Collecting diverse and representative data is crucial to ensure your models are trained to handle real-world scenarios effectively. Here are some methods for achieving diverse and representative data:

  • Stratified sampling: Use this method to ensure all subgroups are represented in your dataset. By stratifying the sampling process, you can capture the distribution of different subsets in the real world.
  • Source data from varied environments: To mirror the real-world conditions your model will encounter, it is important to source data from different environments. This could include different locations, lighting conditions, or any other relevant variables.
  • Data augmentation techniques: Simulate various conditions by augmenting your existing dataset. This involves applying techniques such as random rotations, scaling, or noise addition to create variations in the data.
  • Label Studio Enterprise’s Data Discovery feature can be extremely helpful in this stage of the process as you’re sorting through large sets of unstructured data to identify enough representative data items across your desired classes. You can quickly zero in on the items that fit your criteria and add them to your labeling dataset in a fraction of the time that it would take to sort through items manually.

Determining the Right Dataset Size

Determining the right dataset size is crucial for achieving optimal model performance. The following factors should be considered when determining the appropriate dataset size:

  • Model complexity: More complex models, such as deep neural networks, generally require more data to effectively capture intricate patterns.
  • Task complexity: The complexity of the task at hand also influences the dataset size required. Simpler tasks may need less data, while complex tasks like natural language understanding may demand larger datasets.
  • Data variability: If your data exhibits extensive variability (e.g., images with different lighting conditions), a larger dataset is necessary to capture the full range of variations.
  • Performance plateauing: Continuously increase the dataset size until additional data no longer significantly improves model performance. There will be a point where the model's performance plateaus, indicating the ideal dataset size.
  • Benchmarking: Compare your project with similar projects or datasets in your field to get an estimate of the appropriate dataset size. This can provide valuable insights and help you determine a baseline.

Data Cleaning

Data cleaning is a crucial stage in preparing your data for labeling. It involves the removal of irrelevant, redundant, or low-quality data that may negatively impact model accuracy. While this can feel like a painful step to take, it’s worth it when it comes time to start labeling. It’s fair to note here that if you’re using Data Discovery to build your labeling datasets, this step is largely unnecessary as you will have already eliminated unsuitable items in the Data Collection step.

Removing Irrelevant Data

  • Automated filtering: Utilize scripts or automated tools to filter out data that does not meet specific criteria. For example, you can filter out images with low resolution or text with grammatical errors.
  • Statistical analysis: Employ statistical methods to identify anomalies or irrelevant patterns in the data. This can help you remove data that does not contribute to your objectives.
  • Manual review: In some cases, a manual review of the data may be necessary to assess its relevance and quality. A human reviewer can provide valuable judgment and apply context-specific criteria for data removal.
  • Data deduplication: Use tools to find and remove duplicate entries from your dataset. Duplicate data can skew your results and impact the accuracy of your model.

Handling Missing Values and Outliers

Missing values and outliers can significantly affect the quality of your data. Here are some methods for identifying and handling missing values and outliers:

  • Descriptive statistics: Use measures such as mean, median, and standard deviation to identify outliers in your data. Visualizing the data distribution using box plots, scatter plots, or histograms can also help identify outliers or missing values.
  • Automated detection tools: Employ algorithms or statistical models to automatically detect outliers or missing values based on predefined criteria. These automated tools can save time and provide valuable insights.
  • Imputation or data augmentation: For missing values, you can choose to impute the missing values by estimating them based on the available data. Another approach is data augmentation, where missing values are replaced with synthesized data generated from the existing data.
  • Removing entries: In some cases, it may be appropriate to remove entries with missing values or outliers altogether from your dataset. However, this should be done with caution and after careful consideration of the impact on the overall dataset.

Normalizing Data Formats

Data format consistency is essential for effective data processing. Here are some best practices for normalizing data formats:

  • Establishing standards: Set clear guidelines for data formats early in the project. This ensures consistency and facilitates easy integration of disparate data sources.
  • Conversion tools: Use software tools to convert data into a uniform format. For example, you can convert all dates to the ISO format (YYYY-MM-DD) to ensure consistency across the dataset.
  • Regular expressions: Utilize regular expressions to identify and transform data formats. This is particularly useful for text data that needs to be standardized.
  • Batch processing: For large datasets, batch processing tools can uniformly apply format changes across the entire dataset. This streamlines the normalization process and ensures consistent data formatting.

Data Anonymization

When working with personal data, data anonymization is critical to comply with regulations such as GDPR and HIPAA. Here are some techniques for data anonymization:

  • Pseudonymization: Replace personal identifiers with pseudonyms, ensuring that the original identity cannot be easily linked to the pseudonym.
  • Data masking: Mask sensitive or identifiable information, such as names or addresses, with generalized or fictional data. This ensures individual privacy while maintaining the integrity of the dataset.
  • Data aggregation: Group data into larger categories to reduce the risk of re-identifying individuals. Aggregation helps to preserve the overall insights without compromising individual privacy.

Data Splitting

Now that you’ve found a strong, representative dataset and you’ve cleaned and prepared the data, a common next step is dividing your dataset into training, validation, and test sets. This step is particularly essential for evaluating and validating your machine learning models. Here are some methods for effective data splitting:

Determining Training, Validation, and Test Sets

Common ratios for splitting datasets are 70:15:15 or 60:20:20 for training, validation, and test sets, respectively. These ratios provide an appropriate balance between learning, fine-tuning, and evaluating the model.

  • Random sampling: Randomly select subsets of data for each set. Random sampling ensures that each set is a representative mini-version of the complete dataset, maintaining the overall distribution.
  • Stratified sampling: If your dataset is imbalanced, meaning that certain classes are underrepresented, stratified sampling can help maintain the proportion of different classes in each set. This ensures that the model learns from and is tested on a representative sample from each class.

Ensuring Representativeness in Splits

To ensure the representativeness of your data splits, you can employ the following techniques:

Cross-validation techniques: Use methods like k-fold cross-validation to ensure that each subset of data is used for training and validation. This provides a more robust evaluation of your model and helps prevent overfitting.

  • Consistency checks: Regularly check that the distribution of key features and classes remains consistent across all splits. This helps ensure that each split captures the diversity present in the entire dataset.
  • Stratified sampling: Stratified sampling is particularly important for imbalanced datasets. It ensures that each split contains a proportional representation of different classes, preventing skewness in the evaluation process.


Preparing data for machine learning is a meticulous process that requires careful planning and execution. By following this step-by-step guide, you can ensure that your data is well-prepared, leading to more effective training and more accurate machine learning models. It is essential to remember that the quality of your data labeling directly influences the success of your AI projects.

Take the time to define your objectives, determine the right classes, collect diverse and representative data, clean and normalize your data, ensure data anonymity when necessary, and divide your dataset properly. These steps will lay a solid foundation for successful machine learning model development and, ultimately, foster better decision-making and insights.

And if you’re interested in making this process far more efficient and painless at scale, check out our Data DIscovery feature. It’s designed to ease and speed the process of identifying your ideal items from large bodies of data, so that you can focus on building successful models, not figuring out how to source your next dataset.

Happy labeling!

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