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  1. Stackups
  2. AI
  3. Development & Training Tools
  4. Machine Learning Tools
  5. CUDA vs DataRobot

CUDA vs DataRobot

OverviewComparisonAlternatives

Overview

CUDA
CUDA
Stacks542
Followers215
Votes0
DataRobot
DataRobot
Stacks27
Followers83
Votes0

CUDA vs DataRobot: What are the differences?

Developers describe CUDA as "It provides everything you need to develop GPU-accelerated applications". A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation. On the other hand, DataRobot is detailed as "Lets you accelerate your AI success today with cutting-edge machine learning and the team you have in place". It is an enterprise-grade predictive analysis software for business analysts, data scientists, executives, and IT professionals. It analyzes numerous innovative machine learning algorithms to establish, implement, and build bespoke predictive models for each situation.

CUDA and DataRobot can be primarily classified as "Machine Learning" tools.

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Detailed Comparison

CUDA
CUDA
DataRobot
DataRobot

A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.

It is an enterprise-grade predictive analysis software for business analysts, data scientists, executives, and IT professionals. It analyzes numerous innovative machine learning algorithms to establish, implement, and build bespoke predictive models for each situation.

-
Automated machine learning; Data accuracy; Speed; Ease of use; Ecosystem of algorithms; Data preparation; ETL and visualization tools; Integration with enterprise security technologies; Numerous database certifications; Distributed and self-healing architecture; Hadoop cluster plug and play
Statistics
Stacks
542
Stacks
27
Followers
215
Followers
83
Votes
0
Votes
0
Integrations
No integrations available
Tableau
Tableau
Domino
Domino
Looker
Looker
Trifacta
Trifacta
Cloudera Enterprise
Cloudera Enterprise
Snowflake
Snowflake
Qlik Sense
Qlik Sense
AWS CloudHSM
AWS CloudHSM

What are some alternatives to CUDA, DataRobot?

TensorFlow

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

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

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

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

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

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

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

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

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

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.

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