Genomic data is outpacing traditional Big Data disciplines, producing more information than Astronomy, twitter, and YouTube combined. As such, Genomic research has leapfrogged to the forefront of Big Data and Cloud solutions using artificial intelligence and machine learning to generate insights from these unprecedented volumes of data. This talk hence showcases how we find the disease genes responsible for ALS using VariantSpark, which is a custom random forest implementation built on top of Spark to deal with the 80 million columns in genomic data. This talk also outlines how we use a serverless architecture to translate these insights onto the clinical practice by provide a decision support framework for clinicians to find actionable genomic insights and process medical records at a speed fit for point-of-care application. Furthermore, the talk also touches on how to evolve serverless architecture more efficiently through an hypothesis-driven approach to DevOps and how we keep data and functions secure in a serverless environment.