Accelerating ML Projects with Synthetic Data: How Tools Like evoML Can Help Navigate Data Access Challenges

In the rapidly evolving landscape of machine learning, access to high-quality data is paramount for success. However, real-world data often comes with significant challenges, such as privacy concerns, regulatory restrictions, and the time-consuming processes of data collection and preparation. Synthetic data presents an innovative solution to these challenges, enabling businesses to generate datasets that mirror real-world data while protecting sensitive information.

Synthetic data not only accelerates model development but also opens doors for custom datasets tailored to specific use cases. Tools like evoML make this process even more efficient by offering an AI-powered platform that simplifies synthetic data generation and model prototyping. In this article, we will explore the key advantages of synthetic data in machine learning and how platforms like evoML are reshaping the way businesses handle data scarcity and privacy concerns.

Synthetic data: The what and the why

Synthetic data is data that is either fully or partially artificially generated to mimic real data. Synthetic data can be used in place of real data, which can offer several benefits:

  1. Protecting sensitive real-world data: In many instances, real-world data can contain sensitive information about individuals or entities. In such scenarios, synthetic data can be used in place of real data. Doing so protects sensitive information, while also enabling businesses to comply with any regulatory requirements.
  2. Customised datasets: Synthetic data can be generated with specific features. As a result, customised synthetic datasets can be generated to fit a given use case.
  3. Fast access to datasets: Even large sets of synthetic data can be generated quickly and easily, accelerating data analytics, modelling, and model prototyping.

evoML AI data generator

evoML is a platform that brings the entire data science pipeline onto a single platform. evoML functionalities span from data preprocessing to model deployment, and the process can be implemented with just a few clicks.

Now, in order to make the model prototyping process even more efficient, evoML provides a synthetic data generator powered by LLMs.

Generate synthetic data with 3 easy steps

The synthetic data generation process is quick and easy:

  • Select the AI Sample option on evoML

  • Write your question or use case and click on the Generate use cases button

  • Import your synthetic dataset

What can you do with the synthetic data on evoML?

Once you generate a synthetic dataset, you can use additional functionalities on evoML to get the most out of your dataset. Here are some thing you can do:

Explore your features

Explore the features of your dataset to understand which ones would have the greatest impact on your target feature. You can use this information, paired with your subject matter expertise to regenerate a more targeted dataset if required.

evoML provides various visualisations and metrics for this purpose, see example below:

Build a model to predict your target feature

Use the evoML model building functionality to build a machine learning model to predict your target feature. evoML can help you build a machine learning model within minutes, which can be extremely useful if you are looking to quickly prototype models for a specific use case with less effort.

Evaluate your model

evoML provides a set of options and metrics to evaluate the performance of the model. These metrics will help you decide if a certain model is worth exploring further with additional data or moving forward to deployment.

Two key use cases

Here are two key use cases of synthetic data:

Synthetic data in finance

Information such as trading or company financial data, as well as individual credit data can be difficult to obtain for modelling purposes. However, such data can be incredibly useful for tasks such as investment analysis, portfolio analysis, or consumer finance research. In order to navigate the challenge of data scarcity in these tasks, businesses can resort to using synthetic data.

Synthetic data in healthcare

Patient and medical data is one of the most sensitive data categories. This makes it difficult to access medical data, and working with such data includes significant data protection checks. However, at the same time, data-driven decision-making can bring significant improvements in the healthcare sector. In order to harness the value of data-driven analytics in healthcare, without compromising real-world patient information, businesses can utilise synthetic data.

In summary, synthetic data offers a powerful solution to the challenges of data access and privacy, enabling businesses to innovate and accelerate their machine learning development. With evoML’s synthetic data generator, organisations can quickly create customised datasets that drive meaningful insights while ensuring compliance with data protection regulations.

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