
Statistical knowledge and domain expertise are key to extracting actionable insights from data, yet such skills rarely coexist together. In Machine Learning (ML), high-quality results are only attainable via mindful data preprocessing, hyperparameter tuning and model selection. Domain experts are often overwhelmed by such complexity, de-facto inhibiting wider adoption of ML techniques. Existing libraries claim to solve this problem, but still require well-trained practitioners. Such frameworks involve heavy data preparation steps and are often too slow for interactive feedback from the user, severely limiting the scope of such systems.