Metarank
AutoML style personalized ranking
Metarank is an open-source platform for building and deploying machine learning (ML) models in production environments. It provides a set of tools and workflows for data preprocessing, model training, and model deployment, with a focus on reproducibility, scalability, and ease of use. One of the key features of Metarank is its support for reproducibility in ML workflows. It uses a combination of Docker containers and Git version control to ensure that all code, data, and dependencies are tracked and versioned, making it easy to reproduce past results and compare different versions of a model. Metarank also includes a range of tools for data preprocessing and feature engineering, including support for data validation, cleansing, and normalization. It can handle large and complex datasets, and includes support for distributed computing to enable scalability and speed. For model training, Metarank provides a range of algorithms and frameworks, including support for deep learning and reinforcement learning. It includes support for hyperparameter tuning and model selection, and can automatically generate reports and visualizations to help users understand and interpret the results of their experiments. Finally, Metarank includes a simple and intuitive API for model deployment, with support for both REST and gRPC interfaces. It includes built-in support for model versioning and rolling updates, making it easy to manage and update models in production environments. Overall, Metarank is a powerful and flexible platform for building and deploying ML models at scale. Its open-source architecture and active community also make it a cost-effective alternative to commercial ML Ops solutions.