Paperspace vs wise.io
Overview
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Detailed Comparison
Wise.io builds machine intelligence products that make it easy for companies to derive actionable insight from their greatest corporate resource: their data. | 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. |
Use Wise.io for: Fraud detection, Intelligent sensors, Ad Targeting & Personalization, Genomics, Business Analytics, Finance, Healthcare, Sentiment Analysis;Dead simple machine learning.- Our intuitive, easy-to-use platform for machine learning enables anyone to build and deploy models with a few simple clicks.;A data science marketplace.- With the feature marketplace, we provide companies access to an expansive knowledge base.;State-of the art technology.- Our IP is 10-100x faster and more memory efficient than any other implementation we can find.;From experiment to production.- By breaking the barrier between sandbox learning and large-scale production environments, we decrease the lead time from inception to deployment.;Automated reports.- Every time you build a model, we generate an easy-to-read report detailing the insights gleaned from your data and the performance of your newly minted model.;Public or private cloud.- Our hosted platform makes it easy for businesses to deploy machine intelligence without having to build the infrastructure. For companies with security or latency concerns, we gladly offer an on-premise solution. | Intelligent alert;
Two-factor authentication;
Share drives;
Unlimited power;
Multiple monitors;
Remote access;
Simple management. |
Statistics | |
Stacks 2 | Stacks 4 |
Followers 8 | Followers 20 |
Votes 0 | Votes 0 |
Integrations | |
| No integrations available | |
What are some alternatives to wise.io, 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.
