Greykite vs Ludwig
Overview
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CLI (Node.js)
Manual
Detailed Comparison
Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code. All you need to provide is a CSV file containing your data, a list of columns to use as inputs, and a list of columns to use as outputs, Ludwig will do the rest. | It is a forecast library that allows you to do exploratory data analysis (EDA), forecast pipeline, model tuning, benchmarking, etc. It includes the Silverkite model, a forecast model developed by Linkedin, which allows feature engineering, automatic changepoint detection, holiday effects, various machine learning fitting methods, statitical prediction bands, etc. |
| - | Provides time series regressors to capture trend, seasonality, holidays, changepoints, and autoregression, and lets you add your own;
Fits the forecast using a machine learning model of your choice;
Provides powerful plotting tools to explore seasonality, interactions, changepoints, etc;
Provides model templates (default parameters) that work well based on data characteristics and forecast requirements (e.g. daily long-term forecast);
Produces interpretable output, with model summary to examine individual regressors, and component plots to visually inspect the combined effect of related regressors;
Facilitates interactive prototyping, grid search, and benchmarking. Grid search is useful for model selection and semi-automatic forecasting of multiple metrics;
Exposes multiple forecast algorithms in the same interface, making it easy to try algorithms from different libraries and compare results;
The same pipeline provides preprocessing, cross-validation, backtest, forecast, and evaluation with any algorithm |
Statistics | |
GitHub Stars - | GitHub Stars 1.8K |
GitHub Forks - | GitHub Forks 105 |
Stacks 35 | Stacks 1 |
Followers 101 | Followers 8 |
Votes 0 | Votes 0 |
Integrations | |
What are some alternatives to Ludwig, Greykite?
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.
