Metarank vs Numba
Overview
Share your Stack
Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.
CLI (Node.js)
Manual
Detailed Comparison
It translates Python functions to optimized machine code at runtime using the industry-standard LLVM compiler library. It offers a range of options for parallelising Python code for CPUs and GPUs, often with only minor code changes. | It makes it easy to personalize any listing: recommendations, articles, and search results. Developers make one reranking API call, and Metarank takes care of ML feature updates, model training, and improving target goals like CTR/conversion. |
On-the-fly code generation;
Native code generation for the CPU (default) and GPU hardware;
Integration with the Python scientific software stack | Built-in feature store to compute features used for online and offline training;
REST API, Kafka, Apache Pulsar connectors to receive events and metadata updates;
Offline and online (real-time personalization) operation modes;
Explain mode to understand how final ranking is computed;
Local mode to run Metarank locally without deploying to a cluster;
Cloud native: deploy Metarank to Kubernetes or AWS |
Statistics | |
GitHub Stars 0 | GitHub Stars 2.2K |
GitHub Forks 0 | GitHub Forks 102 |
Stacks 20 | Stacks 2 |
Followers 44 | Followers 9 |
Votes 0 | Votes 0 |
Integrations | |
What are some alternatives to Numba, Metarank?
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
Kubeflow
The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.
TensorFlow.js
Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API
Polyaxon
An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.
Streamlit
It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.
MLflow
MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
H2O
H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.
