MXNet vs UpTrain
Overview
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CLI (Node.js)
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Detailed Comparison
A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. | It is an open-source ML refinement tool for you to understand how your models are working in production. It monitors your model performance and data distributions as the model performs prediction on real-world data. |
Lightweight;Portable;Flexible distributed/Mobile deep learning; | Data Drift Checks - identify distribution shifts in your model inputs;
Performance Monitoring - track the performance of your models in realtime and get alerted as soon as a dip is observed;
Edge Case Signals - user-defined signals and statistical techniques to detect out-of-distribution data-points;
Data Integrity Checks - checks for missing or inconsistent data, duplicate records, data quality, etc;
Customizable metrics - define custom metrics that make sense for your use case;
Automated Retraining - automate model retraining by attaching your training and inference pipelines;
Model Bias - track popularity bias in your recommendation models;
Data Security - your data never goes out of your machine |
Statistics | |
GitHub Stars - | GitHub Stars 2.3K |
GitHub Forks - | GitHub Forks 198 |
Stacks 49 | Stacks 0 |
Followers 81 | Followers 1 |
Votes 2 | Votes 0 |
Pros & Cons | |
Pros
| No community feedback yet |
Integrations | |
What are some alternatives to MXNet, UpTrain?
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.
