MLCon 2019 Spring Archive

Automated Machine Learning is rapidly becoming a pervasive tool for data scientists and machine learning practitioners to quickly build accurate machine learning models.  Recent AutoML products from Google,  Microsoft, AutoSKLearn, Auger.AI and others emphasize a programmatic API approach (versus a visual leaderboard) to applying AutoML. These products attempt hundreds of algorithm/hyperparameters to choose and tune the best predictive model.  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 AutoML, 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.  

To address this, we propose a standardized automated machine learning pipeline: PREDICT (Prediction, Review, Evaluation, Deploy, Import, Configure and Train).  And we walk through a multi-vendor open source project called A2ML (http://github.com/deeplearninc/a2ml) that implements this pipeline for Google Cloud AutoML, Microsoft Azure AutoML, AutoSKLearn, H20 and Auger.AI.  We then show building an application and trained model with multiple AutoML products simultaneously using this standard API.