In the AI and machine learning world, data reigns supreme. But what do you do when real data isn’t sufficient? Perhaps there isn’t sufficient data to work with, perhaps there’s a risk that data might prove to be too sensitive to use effectively, or perhaps the data isn’t diverse enough to effectively train models. It’s in these situations that synthetic data generation comes into play—and it isn’t a buzzword. It’s a real-world, practical answer that’s saving teams loads of time, money, and stress.
Let’s get into five of the best synthetic data generation tools currently in the market today, and why each of these tools excels uniquely.
1. K2view
A standalone, best-in-class offering, K2view isn’t only a perfect fit for synthetic data generation but also a data operations platform in its own right. Its AI-driven generation engine allows for dynamic subsetting of the training data, automated masking of PII, and even synthetic data creation for LLM pipeline use.
On the rules-based end, K2view automates the process of creating high-quality test data in bulk. Auto-generated rules driven by data catalog classification, a user-friendly no-code environment, and full parameter customization grant the tester total control of the data needed—whether functional testing, edge case verification, or stress testing to gauge performance.
In essence, K2view does not merely create synthetic data but creates trusted, accurate data that’s contextually aware and directly integrates into your test environments and ML pipelines. Such is the maturity that has positioned K2view as a Visionary in Gartner’s 2024 Magic Quadrant for Data Integration.
2. Mostly AI
Mostly AI has made a splash for some time now, particularly in sectors in which privacy isn’t up for debate, such as banking, healthcare, and insurance. What sets Mostly AI apart is the seriousness it lends to data privacy—this tool not only creates data that appears legitimate but guarantees that the data that’s produced cannot be traced to real people.
And the best part about it? It employs what’s known as “generative AI,” which learns patterns in your original data and generates new, synthetic data that mirrors those patterns very accurately. You still have all the insights and trends without ever even risk a data breach.
It’s also quite user-friendly for the most part. You do not need to be a machine learning specialist to begin. Structured data such as spreadsheets or database records work well for it, and it performs wonderfully in retaining intricate relationships between various data points—customer actions, for example, or purchase order history or patient data. That’s important when your models need to capture the minute interactions.
3. Synthesis AI
If your application leans towards the visual side—computer vision, face recognition, driver monitoring systems—Synthesis AI is your top pick. Unlike the earlier tools that deal primarily in structured data, Synthesis AI produces hyper-realistic synthetic images and footage of humans.
What makes this all so strong is the amount of control you have. You are able to adjust lighting, environment, race, age, even head tilt or eye gaze. You’re able to build training datasets that capture a diverse world—without having to do a photo shoot for each condition
Synthesis AI also introduces ethics into the equation. It’s meant to eliminate bias in data by enabling you to create inclusive training data. No longer do you have to worry that your face recognition model only performs for a select range of skin tones or ages.
If you’re developing AI that perceives humans, this tool becomes a game-changer.
4. YData
YData isn’t not about creating synthetic data but rather a complete tool for enhancing the overall quality of your data sets. It’s highly data science–friendly, and you’ll adore it if you’re a person that tends to spend a considerable amount of your efforts cleaning data, working with imbalance problems, or attempting to eke out extra performance out of your models.
A major strength of YData is how nicely it adapts to your current workflow. It gets along well with Jupyter Notebooks, has Python support, and allows you to train your own generators against your real datasets. You’re not tied to pre-defined templates or rules—you create what works for your application.
YData also enables you to simulate uncommon events. If you’re developing for fraud prevention or medical diagnostics and you have uncommon but significant situations that are underrepresented in your data, you’re able to augment your data with synthetic examples that’ll still sound real.
To most teams, YData becomes not only a tool, but a complementary part of their pipeline for building their models.
5. Unity Perception
Let’s switch narratives a bit and discuss Unity Perception. If you are in the field of robotics, autonomous vehicles, or working in 3D space in some form or another, you’ve likely already heard of Unity. But Unity Perception lets you essentially build synthetic worlds in which you’re producing the labeled training data used to do computer vision work.
You have the ability to create virtual worlds in which the size of the objects can be randomized, as well as their color, location, lighting—everything. Each frame of data also has perfect labels associated with it, whether bounding boxes or segmentation masks. It’s a vision trainer’s ultimate wish.
One thing that really stands out here is the fact that it scales. You can produce thousands of varied images overnight without the need to hire annotators or purchase costly image datasets. You’re completely in charge, and the results are surprisingly high-quality when done correctly.
Unity Perception does have a slight setup process to get accustomed to, but when you’re in the model-training business for models that have to grasp the 3D world, it’s tough to top.
Winding It Up
As data privacy regulation strengthens and the need for high-quality datasets continues to increase, synthetic data is no longer a “nice to have,” it’s becoming a core component of how AI comes to life. Whether you’re in need of improved training data, desire to steer clear of the headaches associated with the law, or simply cannot acquire adequate real-world samples, the technologies we are exploring here—K2view, Mostly AI, Synthesis AI, YData, and Unity Perception—are the game-changers.
Each tool has a sweet spot. Some are optimal for handling numbers and records about customers, others for faces or 3D simulations. The best one for you will ultimately depend on what you are trying to create. But the good news? There’s a good fit for nearly every use case out there.
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