This project is intended to improve the efficiency, accuracy and scalability of exoplanet data processing pipelines. Exoplanets are planets orbiting stars beyond our solar system. They can be detected through the transit method, where the planet passes in front of its host star, causing a temporary dimming of light. By recognizing patterns and making predictions based on large datasets, ML algorithms can automate tasks, uncover hidden relationships. We will train models using footage of known transients, as well as additional augmented data to prevent model from overfitting and increase its ability to recognize less typical transits.