Our team has the ability to collect, clean, augment, normalize, and standardize real-world data and use that data for prediction using deep learning and machine learning models. We build application-specific solutions based on client requirements.
Implemented multiple projects both on servers and embedded devices such as Raspberry Pi, NVidia Jetson, and ESP-32 using deep learning and computer vision. Tools such as Tensorflow are used to train the models and later implement them using deep learning libraries on servers. We also created APIs to access them based on the requirements.
Developed deep learning models for application-specific tasks such as object detection and voice command recognition and implemented them on microcontrollers and mobile devices using TensorFlow lite.
The team has experience in integrating Ai capabilities into various hardware platforms using the latest tools such as Tiny Machine learning.
Our expertise also includes automated test harness development for GUI applications running across various operating systems. The harness tests the GUI elements of the Application and executes smoke sanity on nightly builds, thus validating the sanity of the application during build cycles.
Research done by Zackriya Solutions
Developed Python based tools which uses Weighted Wavelet Z-Transform that has wide applications in Astronomical data analysis including TESS (Transiting Exoplanet Survey Satellite by NASA) data.
Analysis of data from X-ray Astronomy Satellite, Astrosat using NASA’s HEAsoft tools as well as Python. Also experienced in data science using Python Pandas and Julia DataFrames.
Simulated galaxies using Python based modules, Numpy and Matplotlib to study their dynamics with the Monte Carlo method; scientific computation was carried out using the Python Scipy module.