Recently, a new horizon in data analytics, prescriptive analytics, is becoming more and more important to make data-driven decisions. As opposed to the progress of democratizing data acquisition and access, making data-driven decisions remains a significant challenge for people without technical expertise. In this regard, existing tools for data analytics which were designed decades ago still present a high bar for domain experts, and removing this bar requires a fundamental rethinking of both interface and backend. At Einblick, an MIT/Brown spin-off based on the Northstar project, we have been building the next generation analytics tool in the last few years. To overcome the shortcomings of existing processing engines, we propose Davos , Einblick's novel backend. Davos combines aspects of progressive computation, approximate query processing and sampling, with a specific focus on supporting user-defined operations. Moreover, Davos optimizes multi-tenant scenarios to promote collaboration. Both empirical evaluation and user study verify that Davos can greatly empower data analytics for new needs.

We introduce Ordalia, a novel approach for speeding up deep learning hyperparameter optimization search through early-pruning of less promising configurations. Our method leverages empirical and theoretical results characterizing the shape of the generalization error curve for increasing training data size and number of epochs. We show that with relatively small computational resources one can estimate the dominant parameters of neural networks' learning curves to obtain consistently good evaluations of their learning process to reliably early-eliminate non-promising configurations. By iterating this process with increasing training resources Ordalia rapidly converges to a small candidate set that includes many of the most promising configurations. We compare the performance of Ordalia with Hyperband, the state-of-the-art model-free hyperparameter optimization algorithm, and show that Ordalia consistently outperforms it on a variety of deep learning tasks. Ordalia conservative use of computational resources and ability to evaluate neural networks learning progress leads to a much better exploration and coverage of the search space, which ultimately produces superior neural network configurations.

Statistical knowledge and domain expertise are key to extract actionable insights out of data, yet such skills rarely coexist together. In Machine Learning, 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 a wider adoption of ML techniques in other fields. Existing libraries that claim to solve this problem, still require well-trained practitioners. Those frameworks involve heavy data preparation steps and are often too slow for interactive feedback from the user, severely limiting the scope of such systems.