AutoML is becoming a pervasive tool for data scientists and machine learning practitioners to quickly build accurate machine learning models. Recent products from Google, Microsoft, Auger.AI and others emphasize a programmatic API approach (versus a visual leaderboard) to applying AutoML. All of these products have a similar processing pipeline to achieve a deployed prediction capability: data importing, configuring training, executing training, evaluating winning models, deploying a model for predictions, and reviewing on-going accuracy. With “AutoAutoM”L, ML practitioners can automatically retrain those models based on changing business conditions and discovery of new algorithms. But they are often practically locked into a single AutoML product due to the work necessary to program that particular AutoML product’s API. This talk describes an open source multivendor project called A2ML that allows developers to embed AutoML into their applications with any machine learning vendor’s product.