In our experience of working with domain experts who are using today’s AutoML systems, a common problem we encountered is what we call "unrealistic expectations" – when users are facing a very challenging task with a noisy data acquisition process, while being expected to achieve startlingly high accuracy with machine learning (ML). Many of these are predestined to fail from the beginning. In traditional software engineering, this problem is addressed via a feasibility study, an indispensable step before developing any software system. In this paper, we present Snoopy, with the goal of supporting data scientists and machine learning engineers performing a systematic and theoretically founded feasibility study before building ML applications. We approach this problem by estimating the irreducible error of the underlying task, also known as the Bayes error rate (BER), which stems from data quality issues in datasets used to train or evaluate ML models. We design a practical Bayes error estimator that is compared against baseline feasibility study candidates on 6 datasets (with additional real and synthetic noise of different levels) in computer vision and natural language processing. Furthermore, by including our systematic feasibility study with additional signals into the iterative label cleaning process, we demonstrate in end-to-end experiments how users are able to save substantial labeling time and monetary efforts.

Transfer learning can be seen as a data- and compute-efficient alternative to training models from scratch. The emergence of rich model repositories, such as TensorFlow Hub, enables practitioners and researchers to unleash the potential of these models across a wide range of downstream tasks. As these repositories keep growing exponentially, efficiently selecting a good model for the task at hand becomes paramount. However, a single generic search strategy (e.g., taking the model with the highest linear classifier accuracy) does not lead to optimal model selection for diverse downstream tasks. In fact, using hybrid or mixed strategies can often be beneficial. Therefore, we propose SHiFT, the first downstream task-aware, flexible, and efficient model search engine for transfer learning. Users interface with SHiFT using the SHiFT-QL query language, which gives users the flexibility to customize their search criteria. We optimize SHiFT-QL queries using a cost-based decision maker and evaluate them on a wide rang of tasks. Motivated by the iterative nature of machine learning development, we further support efficient incremental executions of our queries, which requires a special implementation when jointly used with our optimizations.

We demonstrate ease.ml/snoopy, a data analytics system that performs feasibility analysis for machine learning (ML) applications before they are developed. Given a performance target of an ML application (e.g., accuracy above 0.95), ease.ml/snoopy provides a decisive answer to ML developers regarding whether the target is achievable or not. We formulate the feasibility analysis problem as an instance of Bayes error estimation. That is, for a data (distribution) on which the ML application should be performed, ease.ml/snoopy provides an estimate of the Bayes error - the minimum error rate that can be achieved by any classifier. It is well-known that estimating the Bayes error is a notoriously hard task. In ease.ml/snoopy we explore and employ estimators based on the combination of (1) nearest neighbor (NN) classifiers and (2) pre-trained feature transformations. To the best of our knowledge, this is the first work on Bayes error estimation that combines (1) and (2). In today's cost-driven business world, feasibility of an ML project is an ideal piece of information for ML application developers - ease.ml/snoopy plays the role of a reliable " consultant. "