By 2024, 60% of the data used for the development of AI and analytics projects will be synthetically generated
The following quote from Gartner is ubiquitous in articles, decks, and press releases related to synthetic data; even though it’s overused, we still feel that it conveys where the market for synthetic data is going.
In other words, when Gartner says “synthetically generated data” what they mean is that the data is artificial and created rather than coming from real-world sources.
The next, and possibly most important part of this prediction is that synthetic data will be used in the creation of most AI and analytics projects. With more companies adopting these technologies every day, it’s safe to say that the market for synthetic data will continue to grow right along with it.
Synthetic data startups have raised significant amounts of funding and serve a wide range of sectors, from banking and healthcare to transportation and retail. Gartner expects use cases to keep expanding, both inside new sectors as well as those where synthetic data is already common.
If you’re looking to learn more about synthetic data and how it can benefit your business, keep reading.
What Is Synthetic Data?
Synthetic data is a cost-effective and efficient way to generate data for training machine learning models. Unlike real data, which can be expensive and time-consuming to collect, synthetic data is generated by algorithms and is therefore readily available. In addition, synthetic data is not subject to the same privacy concerns as real data, as it is fully anonymous and cannot be traced back to individuals. Synthetic data is also more flexible than real data, as it can be generated to include edge cases and rare events that might not be captured in real-world data sets. As a result, synthetic data can help machine learning models to achieve higher levels of accuracy.
Why Is Synthetic Data So Important?
In recent years, there has been an increased interest in synthetic data. Synthetic data is a digital representation of real data that is generated by a computer. It can be used to train machine learning models without the need for costly and time-consuming data collection processes. In most cases, synthetic data is generated by algorithms that are designed to mimic the underlying distributions of real-world data sets. This process is known as synthetic data generation. There are many benefits of using synthetic data, including the ability to generate large training sets at a fraction of the cost and time required for collecting real data. Additionally, synthetic data can be generated with specific properties that may be difficult or impossible to obtain from real-world data sets. For example, it is often desirable to generate synthetic data that is well-labeled or that covers a wide range of possible values for a particular feature. The use of synthetic data can help machine learning practitioners to overcome some of the challenges associated with working with real-world data sets.
However, synthetic data is not limited to AI and analytics tools. In fact, its impact will be felt much more broadly – extending all the way up to senior decision-makers.
Advantages of Synthetic Data
Synthetic data has a number of advantages over real-world data sets. For one, it is often more accurate, as it can be generated to conform to a known underlying distribution. Because synthetic data can be generated as needed without having to collect and store real data, it is more scalable. Additionally, this solution quickly slashes time-to-market. Finally, synthetic data is easier to use, as it can be generated in a variety of formats and does not require preprocessing. As a result, synthetic data is an increasingly popular choice for training machine learning models.
What Is a Synthetic Dataset?
Synthetic data has a number of advantages over real-world data sets. For one, it is often more accurate, as it can be generated to conform to a known underlying distribution. Because synthetic data can be generated as needed without having to collect and store real data, it is more scalable. Additionally, this solution quickly slashes time-to-market. Finally, synthetic data is easier to use, as it can be generated in a variety of formats and does not require preprocessing. As a result, synthetic data is an increasingly popular choice for training machine learning models.
What Are the Use Cases for Synthetic Data?
Synthetic data is data that is generated by a computer program rather than being collected from real-world sources. While it has many potential applications, synthetic data is often used for testing or training machine learning (ML) models.
One of the advantages of using synthetic data is that it can be generated in large quantities relatively easily. This is important for testing ML models, which often require large training datasets. Synthetic data can also be generated to be very similar to real-world data, which makes it more effective for training ML models.
Another advantage of synthetic data is that it can be generated with specific properties that are desired for testing or training. For example, synthetic data can be generated with known labels, which is not always possible with real-world data. This can be helpful for testing ML models, as it allows for more accurate evaluation of the model’s performance.
Synthetic data remains a powerful tool that can be used in a variety of ways to improve the development and testing of machine learning models.
How Can Synthetic Data Help Computer Vision?
In recent years, the use of synthetic data has become increasingly popular in the field of computer vision. Synthetic data is data that is generated by a computer, as opposed to being collected from the real world. There are several reasons why synthetic data can be helpful for training computer vision models. First, it is often faster and easier to generate large amounts of synthetic data than it is to collect real-world data. This can be especially helpful when trying to train models for rare events or edge cases. Second, synthetic data can be more cost-effective than real-world data, as it does not require the time and effort necessary to collect and label real-world data. Finally, synthetic data can be used to protect sensitive real-world data from being exposed to potentially malicious actors. For example, medical images could be generated from de-identified patient data in order to train a computer vision model without jeopardising patient privacy. In sum, synthetic data can be a valuable tool for training computer vision models, due to its speed, cost-effectiveness, and ability to protect sensitive data.
Synthetic data has a lot of advantages that make it very appealing for use in machine learning projects. Not only does it provide more privacy than real data, but it can also be generated much faster and cheaper. In addition, synthetic data can help with datasets that are imbalanced or have missing values. All of these factors make synthetic data a valuable tool that is worth considering for your next machine learning project.
Beyond Synthetic Data Generation
If you want to not only create synthetic training data, but solve a computer vision problem too, then our platform is perfect for you. SYNIO is the first and only end-to-end solution that takes care of everything from data to model in one place. And, we make it easy for you by doing all the heavy lifting–you just need a 3D object. Within hours, your working AI model will be solving your computer vision problem!
