This talk will describe a NASA Frontier Development Lab research project to analyze satellite data (TESS) for the discovery of exoplanets. The project used time-series TCE data which posed a challenge in the design of our ML application, due to the high volume and added complexity associated with time-series data analytics. This issue was solved by using the programming language kdb+/q. Different models were trained and tested to compare performance due to the complexity of the data. Ultimately a Bayesian Neural Network was chosen, and we obtained 91% accuracy and 83% precision.