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Structured data, or data that adheres to a pre-defined schema, can sufferfrom fragmented context: information describing a single entity can bescattered across multiple datasets or tables tailored for specific businessneeds, with no explicit linking keys (e.g., primary key-foreign keyrelationships or heuristic functions). Context enrichment, or rebuildingfragmented context, using keyless joins is an implicit or explicit step inmachine learning (ML) pipelines over structured data sources. This process istedious, domain-specific, and lacks support in now-prevalent no-code ML systemsthat let users create ML pipelines using just input data and high-levelconfiguration files. In response, we propose Ember, a system that abstracts andautomates keyless joins to generalize context enrichment. Our key insight isthat Ember can enable a general keyless join operator by constructing an indexpopulated with task-specific embeddings. Ember learns these embeddings byleveraging Transformer-based representation learning techniques. We describeour core architectural principles and operators when developing Ember, andempirically demonstrate that Ember allows users to develop no-code pipelinesfor five domains, including search, recommendation and question answering, andcan exceed alternatives by up to 39% recall, with as little as a single lineconfiguration change.

Ember: No-Code Context Enrichment via Similarity-Based Keyless Joins