I have data and a business problem; now what?

In the last few decades, machine learning has made many great leaps and bounds, thereby substantially improving the state of the art in a diverse range of industry applications. However, for a given dataset and a business use case, non-technical users are faced by many questions that limit the adoption of a machine learning solution. For example: • Which machine learning model should I use? • How should I set its hyper-parameters? • Can I trust what my model learned? • Does my model discriminate against a marginalized, protected group? Even for seasoned data scientists, answering these questions can be tedious and time consuming. To address these barriers, the AutoMLx team at Oracle Labs has developed an automated machine learning (AutoML) pipeline that performs automated feature engineering, preprocessing and selection, and then selects a suitable machine learning model and hyper-parameter configuration. To help users understand and trust their "magic" and opaque machine learning models, the AutoMLx package supports a variety of methods that can help explain what the model has learned. In this talk, we will provide an overview of our current AutoMLx methods; we will comment on open questions and our active areas of research; and we will briefly review the projects of our sister teams at Oracle Labs. Finally, in this talk we will briefly reflect on some of the key differences between research in a cutting-edge industry lab compared with research in an academic setting.