Data Generator: experts' secrets for creating quality datasets
Did you know that according to Gartner, 60% of the data used for the development of artificial intelligence will be generated synthetically by 2024? This major evolution places the data generator at the heart of modern AI development strategies.
Indeed, the generation of synthetic data offers considerable advantages. For example, a dataset of only 1,500 synthetic images of Lego bricks made it possible to achieve an accuracy of 88% during the test phase (we invite you to do an online search for this use case: you'll see, it's very interesting!). In addition, the creation of synthetic data significantly reduces costs while improving the quality of labels and the variety of data sets...
💡 In this article, we will explore the essential techniques for creating quality datasets, including using the tools of Synthetic Data Generation. We'll look at how to optimize your AI development processes, from data generation to data validation, to best practices recommended by experts in the field. We will also discuss the importance of monitoring resource consumption and the calculation options available to optimize the performance of synthetic data generators.
Fundamentals of data generation
We begin our exploration of the fundamentals by looking at the different types of synthetic data that form the basis of any data generation process.
Understanding synthetic data types
When it comes to data generation, we distinguish three main categories of synthetic data:
Advantages and limitations of generated data
Indeed, the generation of synthetic data has significant advantages. In particular, it significantly reduces data collection and storage costs. However, it is necessary to meet certain conditions for setting up a pipeline, such as an adequate JSON schema to structure the data generated. In addition, tools like Argilla facilitate the rapid creation of datasets for experiments.
Nonetheless, we need to recognize some limitations. The quality of the data generated depends heavily on the source data. In addition, models may have difficulty accurately reproducing specific cases or anomalies in the original data.
Essential quality criteria
To ensure the excellence of our synthetic datasets, we focus on three fundamental dimensions:
- Loyalty : Measures statistical similarity to original data
- Usefulness : Evaluates performance in downstream applications
- Confidentiality : Checks for the absence of sensitive information leaks
Quality is measured in particular through specific metrics such as the histogram similarity score and the belonging inference score. In this way, we can ensure that our generated data meets the highest quality and safety requirements by providing clear and detailed reference information.
Data generation tools and technologies
Data generation platforms have evolved considerably in recent years. Let's take a look at the various solutions available to create quality datasets together.
Automated generation platforms
In the current landscape, we are seeing a diversity of specialized platforms. Platforms like Mostly AI stand out for their ability to generate synthetic data with remarkable precision, especially in the finance and insurance sectors. At the same time, Gretel offers impressive flexibility with its APIs and pre-built models.
Open-source vs proprietary solutions
To better understand the differences, let's analyze the main characteristics:
Among open-source solutions, we particularly recommend Synthetic Data Vault and Argilla DataCraft (available on Hugging Face), which excel at generating tabular and textual data respectively.
Integrating with ML pipelines
Integrating data generators into ML pipelines is an important aspect. We see that modern ML pipelines are organized into several well-defined steps:
- Data pipeline : Processing user data to create training datasets
- Training pipeline : Training of models using the new datasets
- Validation pipeline : Comparison with the model in production
Therefore, we recommend automating these processes to maintain efficient models in production. Platforms like MOSTLY AI facilitate this automation by offering native integrations with cloud infrastructures, thus making it possible to generate an unlimited or fixed number of synthetic records based on a schema specified by the user.
Additionally, we see that proprietary solutions like Tonic offer advanced features for generating test data that are particularly useful in development environments.
Annotation and validation strategies
Data validation and annotation are key steps in the synthetic data generation process. We are going to explore the strategies that are essential to ensure the quality of our datasets.
Effective annotation techniques
To optimize our annotation process, we use a hybrid approach combining automation and human expertise. There are various options for annotation tools, allowing us to choose the ones that best fit our specific needs. Tools like Argilla allow us to speed up annotation while maintaining high precision. Indeed, the integration of examples annotated by experts can significantly improve the overall quality of a synthetic dataset.
In addition, we are setting up an annotation process in several steps:
- Automatic pre-annotation : Use of AI tools for initial tagging
- Human validation : Review by experts in the field
- Quality control : Checking the consistency of the annotations
Data quality metrics
We use several statistical metrics to assess the quality of our generated data:
The scores from these tests allow us to quantify the quality of the synthetic data, with the aim of reaching a maximum value of 1.0.
Automated validation process
Our automated validation approach is based on three fundamental pillars:
- Statistical validation : Automated tests to verify data distribution
- Consistency check : Verification of relationships between variables
- Anomaly detection : Automatic identification of outliers
In particular, we use validation checkpoints that combine batches of data with their corresponding sets of expectations. This approach allows us to quickly identify potential issues and adjust our generation parameters accordingly.
In addition, we implement ongoing validation processes that monitor data quality in real time. In this way, we can maintain high standards throughout the life cycle of our synthetic datasets.
Optimizing the quality of datasets
Optimizing the quality of synthetic datasets represents a major challenge in our data generation process. We are exploring essential techniques to improve the quality of our datasets.
Balancing data classes
In the context of unbalanced datasets, we use advanced techniques to ensure equitable distribution. Studies show that synthetic datasets correlate positively with the performance of models in pre-training and fine tuning.
We mainly use two approaches:
Management of special cases
With regard to edge cases, we have found that their appropriate management significantly improves the robustness of our models. Specifically, we implement a three-step process:
- Detection : Automatic identification of specific cases
- Triage : Analysis and categorization of anomalies
- Readjustment : Optimization of the model based on the results
💡 Note: special cases often represent less than 0.1% of the data, which requires special attention during their treatment.
Data enrichment
Data enrichment is a critical step in improving the overall quality of our datasets. In light of this need, we use Argilla, a powerful and simple tool, which facilitates the integration of additional information.
Our enrichment strategies include:
- Contextual augmentation : Addition of demographic and behavioral information
- Diversification of sources : Integration of relevant external data
- Ongoing validation : Real-time monitoring of the quality of enriched data
In addition, we observed that a balanced ratio between real and synthetic data optimizes the performance of the models. Also, we are constantly adjusting this ratio according to the results observed.
Automated data enrichment, especially via platforms like Argilla, allows us to achieve remarkable precision while maintaining the integrity of relationships between variables.
Expert best practices
As experts in generating synthetic data, we share our best practices to optimize your data set creation processes. Our experience shows that the success of a data generation project is based on three fundamental pillars.
Workflows we recommend
Our approach to workflows Data generation is based on a structured process. Each phase of the process can be thought of as a separate section, allowing information to be effectively categorized and organized. In fact, synthetic data requires a life cycle in four distinct phases:
At Innovatiana, we regularly use Argilla's DataCraft solution as a data generator for LLM fine-tuning, as it offers remarkable flexibility in creating and validating datasets. However, this tool does not exempt from meticulous review work by specialized experts, in order to produce relevant datasets to train artificial intelligence!
Version Management
Version management is a key part of our process. In addition, we found that successful teams consistently use version control for their datasets. Therefore, we recommend:
- Versioning automated : Use of specialized tools for versioning
- Regular backup : Checkpoints before and after data cleaning
- Traceability of changes : Documentation of changes and their reasons
- Cloud integration : Synchronization with major cloud platforms
In addition, our tests show that versioning significantly improves the reproducibility of results and facilitates collaboration between teams.
Documentation and traceability
Documentation and traceability represent the cornerstone of successful data generation. As a reference, we provide additional information and specific details about each data preparation project. We are implementing a comprehensive system that includes:
- Technical documentation
- Source metadata
- Collection methods
- Applied transformations
- Data dictionary
- Process traceability
- Access logging
- History of changes
- Electronic signatures
- Timestamp of transactions
Traceability is becoming particularly critical in regulated sectors, where we need to prove the compliance of our processes. In addition, we maintain regular audits to ensure the integrity of our synthetic data.
To optimize quality, we conduct periodic reviews of our generation process. These evaluations allow us to identify opportunities for improvement and to adjust our methods accordingly.
In conclusion
The generation of synthetic data is rapidly transforming the development of artificial intelligence. Services, such as watsonx.ai Studio and watsonx.ai Runtime, are critical components for effectively using synthetic data generators. Our in-depth exploration shows that data generators are now essential tools for creating quality datasets.
We looked at the fundamental aspects of data generation, from synthetic data types to essential quality criteria. As a result, we have a better understanding of how platforms like Argilla excel at creating robust and reliable datasets.
In addition:
- The annotation, validation, and optimization strategies presented provide a comprehensive framework for improving the quality of the data generated. Indeed, our structured approach, combining workflows automated systems and expert best practices, guarantees optimal results.
- Meticulous version management and documentation ensure the traceability and reproducibility of our processes. As a result, we strongly recommend adopting these practices to maximize the value of synthetic data in your AI projects.
- This major shift towards synthetic data highlights the importance of adopting these advanced methodologies now. Tools like Argilla facilitate this transition by offering robust solutions that are adaptable to your specific needs.
