Machine Learning (ML) is gaining popularity in many applications. Increasingly, companies prefer more targeted models for different subgroups of the population like locations, which helps improve accuracy. This practice is comparable to Group-By aggregation in SQL; we call it learning over groups. A smaller group means the data distribution is more straightforward than the whole population. So, a group-level model may offer more accuracy in many cases. Non-technical business needs, such as privacy and regulatory compliance, may also necessitate group-level models. For instance, online advertising platforms would need to build disaggregated partner-specific ML models, where all partner groups' training data are aggregated together in one data pipeline.

Many applications that use large-scale machine learning (ML) increasingly prefer different models for subgroups (e.g., countries) to improve accuracy, fairness, or other desiderata. We call this emerging popular practice learning over groups , analogizing to GROUP BY in SQL, albeit for ML training instead of SQL aggregates. From the systems standpoint, this practice compounds the already data-intensive workload of ML model selection (e.g., hyperparameter tuning). Often, thousands of models may need to be trained, necessitating high-throughput parallel execution. Alas, most ML systems today focus on training one model at a time or at best, parallelizing hyperparameter tuning. This status quo leads to resource wastage, low throughput, and high runtimes. In this work, we take the first step towards enabling and optimizing learning over groups from the data systems standpoint for three popular classes of ML: linear models, neural networks, and gradient-boosted decision trees. Analytically and empirically, we compare standard approaches to execute this workload today: task-parallelism and data-parallelism. We find neither is universally dominant. We put forth a novel hybrid approach we call grouped learning that avoids redundancy in communications and I/O using a novel form of parallel gradient descent we call Gradient Accumulation Parallelism (GAP). We prototype our ideas into a system we call Kingpin built on top of existing ML tools and the flexible massively-parallel runtime Ray. An extensive empirical evaluation on large ML benchmark datasets shows that Kingpin matches or is 4x to 14x faster than state-of-the-art ML systems, including Ray's native execution and PyTorch DDP.

Speech-driven querying is becoming popular in new device environments such as smartphones, tablets, and even conversational assistants. However, such querying is largely restricted to natural language. Typed SQL remains the gold standard for sophisticated structured querying although it is painful in many environments, which restricts when and how users consume their data. In this work, we propose to bridge this gap by designing a speech-driven querying system and interface for structured data we call SpeakQL. We support a practically useful subset of regular SQL and allow users to query in any domain with novel touch/speech based human-in-the-loop correction mechanisms. Automatic speech recognition (ASR) introduces myriad forms of errors in transcriptions, presenting us with a technical challenge. We exploit our observations of SQL's properties, its grammar, and the queried database to build a modular architecture. We present the first dataset of spoken SQL queries and a generic approach to generate them for any arbitrary schema. Our experiments show that SpeakQL can automatically correct a large fraction of errors in ASR transcriptions. User studies show that SpeakQL can help users specify SQL queries significantly faster with a speedup of average 2.7x and up to 6.7x compared to typing on a tablet device. SpeakQL also reduces the user effort in specifying queries by a factor of average 10x and up to 60x compared to raw typing effort.

Accelerating machine learning (ML) over relational data is a key focus of the database community. While many real-world datasets are multi-table, most ML tools expect single-table inputs, forcing users to materialize joins before ML, leading to data redundancy and runtime waste. Recent works on ''factorized ML'' address such issues by pushing ML through joins. However, they have hitherto been restricted to ML models linear in the feature space, rendering them less effective when users construct non-linear feature interactions such as pairwise products to boost ML accuracy. In this work, we take a first step towards closing this gap by introducing a new abstraction to enable pairwise feature interactions in multi-table data and present an extensive framework of algebraic rewrite rules for factorized LA operators over feature interactions. Our rewrite rules carefully exploit the interplay of the redundancy caused by both joins and interactions. We prototype our framework in Python to build a tool we call MorpheusFI. An extensive empirical evaluation with both synthetic and real datasets shows that MorpheusFI yields up to 5x speedups over materialized execution for a popular second-order gradient method and even an order of magnitude speedups over a popular stochastic gradient method.

In this demonstration, we present SpeakQL, a speech-driven query system and interface for structured data. SpeakQL supports a tractable and practically useful subset of regular SQL, allowing users to query in any domain with unbounded vocabulary with the help of speech/touch based user-in-the-loop mechanisms for correction. When querying in such domains, automatic speech recognition introduces countless forms of errors in transcriptions, presenting us with a technical challenge. We characterize such errors and leverage our observations along with SQL's unambiguous context-free grammar to first correct the query structure. We then exploit phonetic representation of the queried database to identify the correct Literals, hence delivering the corrected transcribed query. In this demo, we show that SpeakQL helps users reduce time and effort in specifying SQL queries significantly. In addition, we show that SpeakQL, unlike Natural Language Interfaces and conversational assistants, allows users to query over any arbitrary database schema. We allow the audience to explore SpeakQL using an easy-to-use web-based interface to compose SQL queries.