Speaker "Adam Blum" Details Back



Towards a Standardized Automated Machine Learning Pipeline API 


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
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.  


Adam Blum is the CEO of Auger.AI.  He has been CTO/CEO/co-founder of various successful startups. He has been adjunct professor at UC Berkeley and Carnegie Mellon University.  He published the first applied machine learning book (Neural Networks in C++, Wiley, 1992) and holds several patents.