ZeroML: A Next Generation AutoML Language
This addresses inefficiencies in AutoML workflows for both non-coders and ML professionals, though it appears incremental as an architectural improvement over existing tools.
The authors tackled the problem of slow-running time, brittle pipelines, and high dependency costs in existing AutoML tools by introducing ZeroML, a new compiled multi-paradigm programming language with a pure functional core that enables fast, reproducible model creation with high accuracy.
ZeroML is a new generation programming language for AutoML to drive the ML pipeline in a compiled and multi-paradigm way, with a pure functional core. Meeting the shortcomings introduced by Python, R, or Julia such as slow-running time, brittle pipelines or high dependency cost ZeroML brings the Microservices-based architecture adding the modular, reusable pieces such as DataCleaner, FeatureEngineer or ModelSelector. As a native multithread and memory-aware search optimized toolkit, and with one command deployability ability, ZeroML ensures non-coders and ML professionals to create high-accuracy models super fast and in a more reproducible way. The verbosity of the language ensures that when it comes to dropping into the backend, the code we will be creating is extremely clear but the level of repetition and boilerplate required when developing on the front end is now removed.