BigML vs Paperspace
Overview
🔥 Trending in Text & Language Models on StackShare
Detailed Comparison
BigML provides a hosted machine learning platform for advanced analytics. Through BigML's intuitive interface and/or its open API and bindings in several languages, analysts, data scientists and developers alike can quickly build fully actionable predictive models and clusters that can easily be incorporated into related applications and services. | It is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use it to iterate faster and collaborate on intelligent, real-time prediction engines. |
REST API; bindings in Pyton, Java, Ruby, node.js, C#, Clojure, PHP, and more; several algorithms, including categorical & regression decision trees, ensembles of trees (random decision forest), cluster analysis and more; models are fully actionable -- translated into code that can be cut/paste for local utilization; PredictServer (and Amazon AMI) can be used for real-time or large batch predictions; models can be shared privately or publicly (for free or for a fee set by the developer) | Intelligent alert;
Two-factor authentication;
Share drives;
Unlimited power;
Multiple monitors;
Remote access;
Simple management. |
Statistics | |
Stacks 14 | Stacks 4 |
Followers 29 | Followers 20 |
Votes 1 | Votes 0 |
Pros & Cons | |
Pros
| No community feedback yet |
Integrations | |
| No integrations available | |
What are some alternatives to BigML, Paperspace?
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/
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
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.
Inferrd
It is the easiest way to deploy Machine Learning models. Start deploying Tensorflow, Scikit, Keras and spaCy straight from your notebook with just one extra line.
