metarank
Ranking service
A low-code, scalable Machine Learning service for building personalized search and recommendations rankings
A low code Machine Learning personalized ranking service for articles, listings, search results, recommendations that boosts user engagement. A friendly Learn-to-Rank engine
2k stars
17 watching
88 forks
Language: Scala
last commit: over 1 year ago automldata-engineeringdata-sciencedeep-learningfeature-engineeringfeature-extractionkubernetesmachine-learningneural-networkspersonalizationrankingscalasearch
Related projects:
| Repository | Description | Stars |
|---|---|---|
| | Automated machine learning for production and analytics | 1,642 |
| | Calculates rankings in networks using the SpringRank method | 54 |
| | Provides an infrastructure for machine learning in R, enabling users to focus on experiments without writing lengthy wrappers and boilerplate code. | 1,648 |
| | A repository providing MatLab/Octave examples and explanations of popular machine learning algorithms | 857 |
| | A collection of efficient Learning-to-Rank algorithms implemented in C++ | 131 |
| | A meta search engine built on top of Rust with features like theming, multi-language support and advanced filtering options. | 769 |
| | A collection of machine learning algorithms implemented in Scala for prototyping and experimentation. | 39 |
| | Provides an object-oriented framework for efficient machine learning in R | 952 |
| | An AI service for efficient indexing and querying of datasets using LLMs and natural language processing techniques. | 1,660 |
| | A machine learning library built on top of Apache Spark for scalable and flexible regression modeling | 793 |
| | An MLOps platform providing tools and services for efficient machine learning development and deployment | 214 |
| | A repository containing code for a meta-learning experiment on image datasets | 150 |
| | This is an implementation of a meta-learning algorithm to address class imbalance issues in deep learning models with noisy labels. | 284 |
| | Provides tools and datasets for meta-learning and few-shot learning in deep learning | 1,996 |
| | Provides tools and models for building and comparing meta learning recommendation systems in Python. | 23 |