Truss vs XGBoost
Overview
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Detailed Comparison
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow | It is a seamless bridge from model development to model delivery. Truss presents an open-source standard for packaging models built in any framework for sharing and deployment in any environment, local or production. |
Flexible;
Portable;
Multiple Languages;
Battle-tested
| Turns your Python model into a microservice with a production-ready API endpoint, no need for Flask or Django;
For most popular frameworks, includes automatic model serialization and deserialization;
Freezes dependencies via Docker to make your training environment portable;
Enables rapid iteration with local development that matches your production environment;
Encourages shipping parsing and even business logic alongside your model with integrated pre- and post-processing functions;
Bundles secret management to securely give your model access to API keys |
Statistics | |
GitHub Stars 27.6K | GitHub Stars 1.1K |
GitHub Forks 8.8K | GitHub Forks 90 |
Stacks 192 | Stacks 1 |
Followers 86 | Followers 1 |
Votes 0 | Votes 0 |
Integrations | |
What are some alternatives to XGBoost, Truss?
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.
