mltek

mltek is a modular open-source machine learning toolkit designed to support experimentation, evaluation, and deployment of ML models across different platforms. The project emphasizes composability over monolithic design, enabling researchers and developers to assemble pipelines tailored to their specific use cases.

The core components of mltek typically include a data pipeline for loading and transforming datasets, a model abstraction that can wrap neural networks or traditional machine learning models, a training loop with common optimizers and schedules, and a lightweight inference API. It offers facilities for experiment tracking, configuration management, and model serialization to portable formats. Interoperability with popular data formats and libraries, such as NumPy arrays and pandas data frames, is a common design goal, along with support for CPU and optional GPU acceleration.

Architecture and extensibility in mltek are built around a plugin system. Data loaders, model backends, optimization strategies, and evaluation metrics can be implemented as plug-ins, enabling users to extend functionality without modifying core code. The runtime is designed to be embeddable, allowing mltek components to be integrated into larger software stacks or deployed as microservices. Cross-platform compatibility is typically pursued, with bindings for languages such as Python and C++, and optional accelerators to leverage hardware resources.

Development and usage patterns for mltek are community-driven. Documentation and examples aim to assist both newcomers and experienced researchers, outlining steps to define data processing pipelines, select model types, configure training parameters, run experiments, and export trained models for inference in production environments.