AI
SYNTHETIC DATA
We Create Synthetic Data
Our approach is straightforward and direct
1. Simulation
Simulation refers to the use of models and algorithms to imitate real-world processes or systems. It involves creating virtual environments, using both 2D, 3D and game engine applications.
4. Training Model/Brain
Training a model, whether it's a machine learning model or a neural network-based model (often referred to as a "brain" in the context of AI), involves the process of allowing the model to learn from data.
2. Dataset Creation
The dataset is a product of the simulation, which can be represented through code or imagery. Creating synthetic datasets offers the advantage of being potentially unlimited in number, depending on your training needs.
5. Application
Once the model is trained it's tested on the desired real world dataset.
3. Dataset Processing
Processing a synthetic dataset is crucial to ensure that it behaves realistically and retains the desired characteristics for training or testing machine learning models. The specific steps may vary depending on the nature of the synthetic data and the goals of the analysis or modelling tasks.
6. Deployment
Once satisfied with the applications performance, we deploy it to make predictions on new, unseen data in a real-world environment on clients Hardware.
Synthetic Data involves the creation of models and environments that closely mimic real world assets. The below image is an example of a use case where an AI model could be trained to identify individuals on a construction site who are not adhering to strict safety guidelines regarding the use of hard hats.
PASSENGER COUNTING
Passenger Counting involves the creation of models that closely mimic real world assets.
USE CASE SYNTHETIC DATA GENERATION
Synthetic data generation has a wide range of use cases across various industries, and it is employed for different purposes from passenger counting on public transport to airport terminals and stock checks on supermarket shelves. In all these cases the key is to strike a balance between creating synthetic data that accurately represents the characteristics of real world data while ensuring privacy and compliance with regulations. Validation and testing against real data are critical steps to ensure the effectiveness and reliability of models trained on synthetic datasets.
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AREAS OF INTEREST
Transport-Healthcare-Finance-Cybersecurity-Manufacturing and Industrial Processes-Climate and Environmental Modeling-Employee Training-Automotive Industry-Retail-Chatbots and Virtual- Assistants-Telecommunications-Smart Cities and Urban Planning-Traffic Management
