About Us
AI Data Lens Ltd. is a company that paces with the world, inventing something brand new in this data-driven innovative world. Founded by and for innovators, based on very vital principles of precision, innovation, and customer-centricity, we work to empower companies with unlocking the full potential of their data. We specialize in providing comprehensive AI data services that span the entire data lifecycle—from data collection and annotation to advanced analytics without leaving MLOps and beyond. Our purpose is to empower organizations with the power of data across industries in order to drive smarter decision-making, operational efficiency, and innovation.
Gallery
Social Account
AI Model Evaluation and Testing Data
The strength of an AI model is only as strong as the data used to evaluate and test it. At AI Data Lens Ltd, we provide end-to-end services in AI Model Evaluation and Testing Data with a focus on rigorous testing and validation of your models. These range from creating test data representative of real-world scenarios, crafting adversarial test datasets that will better challenge your model, providing validation data for fine-tuning, and we are great at cross-validation dataset preparation that enables robust model evaluation. With these value-added services provided, we are committed to helping you in building AI models that, besides being accurate on a non-realistic test dataset, will prove resilient and reliable in real-world applications while never compromising on the highest level of performance.
Preparation of Test Datasets
We value testing your AI models in environments similar to real-world scenarios at AI Data Lens Ltd. Our dataset preparation services for tests are designed in such a way that we provide prepared data which replicates well the particular scenario that your model is likely to face during its deployment. Hence, we choose such a dataset very carefully and prepare it with the inclusion of diverse examples to challenge your model. This holistic approach helps you identify some weak points and fine-tunes your model to comply with it before it goes live, increasing its reliability and effectiveness.
Adversarial Testing Datasets
All this is an important step in arbitrating robustness within the AI models. AI Data Lens Ltd. has specialized in creating adversarial test datasets, providing insight into any weakness within your model. By introducing subtle, calculated perturbations into the data with care, our adversarial datasets test your model for robustness against attacks and surprise inputs. This will help not only in rigorous testing to find security flaws but also make your AI models resistant to adversarial threats, thereby assuring their reliable functioning under diverse and challenging environments.
Validation Data for Model Tuning
Validation data plays an important role in fine-tuning AI models and bringing them up to the best performance. Here at Ai Data Lens Ltd, we provide you with curated validation datasets that will let you evaluate the general accuracy of your model during training. Preprocessed validation data here could retain the variability of real-world scenarios regarding finding overfitting, tuning hyperparameters, and enhancing model generalization. You’ll validate your AI models using our comprehensive datasets to ensure they are not just accurate but ready to perform well in most user cases.
Cross-Validation Dataset Preparation: For more robust model evaluation
To serve an individual for more robust model evaluation. Cross-validation generally is a formidable method for assessing the reliability and robustness of AI models. AI Data Lens Ltd. proposes cross-validation dataset preparation to allow you to test your model in many ways by drawing different subsets of your data. We create stratified and randomized splits to make sure each fold will be well representative of the whole dataset. This approach provides a more comprehensive evaluation of your model’s capabilities, reducing the risk of overfitting and improving its generalizability. By employing cross-validation, you can be confident that your AI models will perform well in real-world applications, even when faced with new and unseen data.