When we talk about artificial intelligence systems - especially those based on machine learning and computer vision - it's not just about complex algorithms, but also about what we use to train them. Data is the fuel that powers the models. Relevant, diverse and well-labeled datasets are often the key to success.
However, collecting large sets of real data can be difficult, expensive and time-consuming. That's why more and more projects are turning to an alternative: synthetic data.
In this article, we'll look at what data AI needs and why solutions like SKY ENGINE AI make sense in the modern AI world.
What AI models need? Data Types.
Depending on the task, AI requires different types of data. For computer vision and image/video analysis, the following are needed:
- Images or video sequences - with different objects, perspectives, lighting conditions and environmental changes.
- Diversity of scenarios - different camera angles, different contexts, backgrounds, objects, lighting, weather conditions, typical and edge cases.
- Accurate annotations (ground truth) - e.g., bounding boxes, segmentation masks (semantic or instance), depth maps, normal maps, 3D keypoints, object metadata, motion/position/frame order information.
- If required multimodality of sensors- not only RGB, but also other data types, e.g., NIR, thermal, LiDAR, specialized sensors, etc.
- Balance and representativeness - the dataset should represent a variety of situations, scenarios and cases: common and rare, typical and extreme.
Without these elements, an AI model can easily “overlearn” under specific conditions and therefore generalize poorly to real, new data.
Why is real data often insufficient to meet this demand?
Collecting real data - though traditionally the most common - faces many challenges:
- Cost and time - acquiring large numbers of images/videos from various scenarios, plus manual annotation, is a difficult and expensive task.
- Lack of representation for rare cases (edge cases) - e.g., unusual angles, difficult lighting conditions, rare situations - may occur rarely but are critical to the model.
- Issues with privacy, regulations, image rights and data availability - especially in medicine, security, industry and regulated sectors.
- Scalability - as a project grows, data needs change - a new object, a new class, a new sensor- it becomes more difficult to continuously supplement real data.
Therefore, more and more teams are turning to synthetic data - as an extension or alternative to real data.
The Role of synthetic data – what SKY ENGINE AI offers?
Synthetic data is becoming one of the most important pillars of artificial intelligence development, especially in the area of computer vision. SKY ENGINE AI takes a technologically mature approach, offering a platform that allows the generation of incredibly realistic images and video sequences in 3D environments, created with attention to physics, light, materials and object behavior.
Instead of relying solely on costly, difficult-to-collect real-world data, users are empowered to create entire virtual worlds – from industrial simulations, through complex urban scenarios, to medical diagnostic environments. Each scene can be modified almost limitlessly: time of day, weather, camera position, sensor type and object behavior can be modified.
Importantly, the platform immediately generates a full set of annotations - including segmentation, depth, normal maps, 3D data and metadata - which in a traditional process would require a tremendous amount of manual work.
SKY ENGINE AI provides a tool that allows you to build exactly the datasets your project needs: diverse, rich and fully controllable. It's this flexibility and scalability that makes synthetic data go from a curiosity to a real standard in modern MLOps - especially where speed, quality and data security are paramount.
Will synthetic data completely replace real data? Balance and sound criticism
Synthetic data is a powerful tool, but not always a complete replacement for real data. In many cases, the best results are achieved with a hybrid approach:
- synthetic data provides scale, diversity, annotations and edge cases;
- real data provides authenticity - natural variability, the unpredictability of the world, noise and real-world conditions.
This combination allows you to build models that generalize well but are also based on realistic data. It's also worth remembering that while synthetic generation technology has improved significantly and platforms like SKY ENGINE AI offer advanced simulations, there's always a risk that the real world will differ from the generated scenes. Therefore, thorough preparation, testing and validation are the most common-sense approach.
Data is the foundation, synthetics are the tool
If you're planning an AI project-especially in the areas of computer vision, detection, segmentation, image analysis, or video-consider what type of data you need. Manually generated images and data are often insufficient or too expensive. Synthetic data-generated with platforms like SKY ENGINE AI allows for scaling, experimentation, edge-case testing and building robust, well-labeled datasets.
A well-chosen dataset-real, synthetic, or mixed-is often the key to AI success. And while models, architectures and algorithms are important, data remains the foundation.