Synthetic data for ML: the game-changer in training for 2025

Machine learning models are only as good as the data they are trained on. But in 2025, the challenge isn’t just having enough data; it’s about having the right data. Real-world datasets are often scarce, expensive to label, and risky to share because of privacy laws. Manual labeling of real-world data is both costly and time-consuming, making it a significant barrier for many organizations; synthetic data provides a cost-effective alternative by reducing the cost and effort required for data annotation. Additionally, real-world datasets frequently underrepresent certain demographics, which can negatively impact model fairness and generalizability. That’s why synthetic data is emerging as one of the most powerful tools for scaling AI.

As we explained in our blog on why data labeling is essential for modern AI, reliable datasets are the foundation of any model. And while labeled data still powers the most advanced AI models, organizations are now using synthetic data to solve challenges that real data alone cannot.

This blog explores why synthetic data is a necessity in 2025, how it’s being used in practice, where it delivers the most value, and the pitfalls to avoid.

Why synthetic data in 2025 is no longer optional

Analysts and industry leaders agree: synthetic data is no longer experimental. Gartner forecasts that by 2030, synthetic data will be more widely used for AI training than real-world datasets.

Major AI firms are moving fast in this direction. A company can utilize synthetic data platforms to enhance its quality assurance processes by generating diverse defect images, which significantly improves defect detection accuracy and reduces costs. Nvidia and Databricks have built scalable synthetic data pipelines to power perception AI across industries. Nvidia’s GTC 2025 announcements, including Cosmos and Isaac GR00T, highlight how simulation-driven training is becoming essential for robotics and physical AI (AP News). Building physical AI models for autonomous systems requires huge amounts and vast amounts of high-quality data, which can be challenging to acquire. Synthetic data addresses this challenge by providing a scalable alternative for handling rapidly changing, sensitive, or inaccessible datasets. Its cost effectiveness compared to real data is clear, as it reduces ongoing expenses related to data collection, revision, and compliance.

Synthetic data offers practical solutions for data scarcity, privacy, and cost challenges, and is not a trend; it is becoming a core strategy for companies scaling machine learning in regulated and data-hungry environments.

Where synthetic data works best

1. Autonomous vehicles
   Real driving datasets can’t capture every rare or dangerous scenario. Simulation pipelines generate billions of miles worth of edge cases, from night-time weather to unusual traffic signals, enabling safer training before deployment. Synthetic data is especially valuable for simulating rare events that are difficult to observe in real-world driving, improving the robustness of autonomous vehicle models.
2. Healthcare
   Synthetic medical records and imaging datasets allow teams to train diagnostic models without exposing patient data, supporting HIPAA and GDPR compliance.
3. Human action recognition
   Synthetic motion video pipelines such as SynthDa fill gaps in real datasets, ensuring better coverage of rare or hard-to-capture movements. This approach also aids in identifying rare defects or uncommon actions that may not be present in limited real-world samples.
4. LLMs and generative AI
   Synthetic text and task prompts are increasingly used for fine-tuning, evaluation, and red-teaming, aligning with the practices we outlined in our [complete guide to Reinforcement Learning from Human Feedback (RLHF)]. AI generated content is also leveraged for model evaluation and to prevent model collapse. Additionally, knowledge bases are used to generate and filter synthetic data, enhancing the contextual relevance and diversity for large language models.

Synthetic data excels in situations where privacy, scale, or coverage gaps make real-world data impractical.

The risks you must manage

While synthetic data opens new possibilities, it also comes with challenges:

• Lack of realism: synthetic examples may miss subtle patterns, reducing performance on real-world tasks.
• Bias amplification: poorly designed generators can reproduce or exaggerate existing biases (TechRadar). There is also a risk of underrepresenting certain demographics in synthetic datasets, which can impact model fairness and generalizability.
• Validation complexity: models trained on synthetic data still need benchmarking against trusted, real-world datasets. Comparing model outputs to ground truth data is essential to ensure quality and reliability. Additionally, manual labeling of synthetic data can be time consuming and resource intensive.

That is why organizations still need human oversight and evaluation loops, echoing the role of reviewers we highlighted in our RLHF content.

Evaluating synthetic data

Ensuring the quality of synthetic data is essential for building reliable machine learning models. Companies must rigorously evaluate their synthetic datasets to confirm that they accurately represent real world data and are suitable for training models that will perform well in real world scenarios.

Key metrics for evaluating synthetic data include accuracy, diversity, and realism. Accuracy measures how closely the synthetic dataset matches the characteristics of the real dataset it is meant to represent. Diversity assesses whether the synthetic data covers a wide range of scenarios and edge cases, helping models identify patterns that might be rare or underrepresented in real data. Realism focuses on how convincingly the synthetic data mimics real world information, ensuring that models trained on synthetic data can generalize effectively.

To evaluate synthetic data, companies often use a combination of automated and manual methods. Data visualization tools can help identify patterns, anomalies, or gaps in the synthetic dataset. Statistical analysis allows for a quantitative comparison between synthetic and real world data, highlighting any discrepancies. Human evaluation remains crucial for assessing the realism and diversity of synthetic data, especially in complex tasks where subtle differences can impact model performance.

By systematically evaluating synthetic datasets, organizations can ensure that their machine learning models are trained on data that is both representative and robust, ultimately leading to better performance in real world applications.

Integrating synthetic data with human-in-the-loop

Combining synthetic data with human-in-the-loop (HITL) processes is a powerful solution for building more accurate and reliable machine learning models. This approach leverages the strengths of both automated synthetic data generation and human expertise, creating a feedback loop that continuously improves training data quality.

For example, companies can use synthetic data to quickly generate large volumes of training data, covering a wide range of scenarios and edge cases that might be rare or difficult to capture in the real world. Human annotators then review, validate, and refine this synthetic data, correcting errors and ensuring that the dataset accurately represents real world situations. This process not only boosts the accuracy of machine learning models but also helps identify and address potential biases in the synthetic dataset.

Integrating synthetic data with HITL enables organizations to create training datasets that are both comprehensive and trustworthy. By combining the scalability of synthetic data generation with the nuanced judgment of human reviewers, companies can develop machine learning models that perform reliably across diverse real world scenarios.

Best practices for using synthetic data in 2025

For synthetic data to deliver value, it must be implemented carefully:

1. Blend synthetic with real data
   Always start with a real dataset as a seed. Use synthetic generation to expand edge cases or cover underrepresented classes. Synthetic data can significantly reduce the cost and time-consuming nature of manual data collection and annotation.
2. Validate on hold-out real data
   Never evaluate performance solely on synthetic sets; always measure against real-world benchmarks.
3. Build governance into pipelines
   Treat synthetic datasets with the same rigor as labeled data: audit, document, and retrain regularly.
4. Match tools to modality
   Use simulation engines for computer vision, generative models for text/tabular, and hybrid pipelines where necessary. Adjusting parameters such as lighting, object placement, and color enables the creation of diverse synthetic datasets, improving model robustness.

Quick implementation checklist

• Define where synthetic data adds the most value (privacy, scale, edge cases).
• Generate synthetic datasets seeded from real-world data.
• Validate models against real hold-out data.
• Audit synthetic outputs for bias and realism.
• Retrain models periodically to prevent drift.
• Use cost-effective synthetic data to scale AI training.
• Document processes for compliance and governance.

The future of synthetic data generation

The future of synthetic data generation is set to transform the landscape of machine learning and AI development. As generative AI and large language models continue to advance, companies will be able to generate synthetic data that is more realistic, diverse, and tailored to specific applications than ever before.

One major trend is the use of large language models to generate synthetic datasets for natural language processing and beyond. These models can create high quality synthetic data that mirrors the complexity and nuance of real world information, enabling more effective training of AI models. Another exciting development is the rise of multimodal synthetic data generation, where images, videos, and text are combined to create rich, diverse datasets that better represent the complexity of real world environments.

Industries such as healthcare, finance, and autonomous vehicles are already beginning to adopt synthetic data generation at scale, recognizing its potential to provide massive datasets for training AI models while maintaining data privacy and reducing costs. As tools and techniques continue to evolve, companies will be able to generate synthetic datasets that not only improve model accuracy but also unlock new applications and use cases that were previously out of reach.

In short, synthetic data generation is poised to become an essential part of the AI development toolkit, enabling organizations to create, train, and deploy machine learning models with unprecedented speed, accuracy, and flexibility.

Conclusion

By 2025, synthetic data has moved beyond hype. It is now an operational necessity for scaling AI responsibly. When used alongside high-quality real-world labels, synthetic datasets can reduce costs, improve coverage, and unlock safer, privacy-compliant machine learning.

For teams looking to future-proof their AI pipelines, the answer is not to replace real data but to combine it with synthetic data strategically.

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