Metarank vs Replicate
Overview
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CLI (Node.js)
Manual
Detailed Comparison
It lets you run machine learning models with a few lines of code, without needing to understand how machine learning works. | 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. |
Thousands of models, ready to use;
Automatic API;
Automatic scale;
Pay by the second | 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 - | GitHub Stars 2.2K |
GitHub Forks - | GitHub Forks 102 |
Stacks 53 | Stacks 2 |
Followers 12 | Followers 9 |
Votes 0 | Votes 0 |
Integrations | |
What are some alternatives to Replicate, Metarank?
Git
Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.
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.
Mercurial
Mercurial is dedicated to speed and efficiency with a sane user interface. It is written in Python. Mercurial's implementation and data structures are designed to be fast. You can generate diffs between revisions, or jump back in time within seconds.
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.
SVN (Subversion)
Subversion exists to be universally recognized and adopted as an open-source, centralized version control system characterized by its reliability as a safe haven for valuable data; the simplicity of its model and usage; and its ability to support the needs of a wide variety of users and projects, from individuals to large-scale enterprise operations.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
NanoNets
Build a custom machine learning model without expertise or large amount of data. Just go to nanonets, upload images, wait for few minutes and integrate nanonets API to your application.
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
