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

DataRobot vs Lobe

OverviewComparisonAlternatives

Overview

DataRobot
DataRobot
Stacks27
Followers83
Votes0
Lobe
Lobe
Stacks1
Followers18
Votes0

Lobe vs DataRobot: What are the differences?

Lobe: Deep learning made simple. An easy-to-use visual tool that lets you build custom deep learning models, quickly train them, and ship them directly in your app without writing any code; DataRobot: 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.

Lobe and DataRobot belong to "Machine Learning Tools" category of the tech stack.

Some of the features offered by Lobe are:

  • Build - Drag in your training data and Lobe automatically builds you a custom deep learning model. Then refine your model by adjusting settings and connecting pre-trained building blocks.
  • Train - Monitor training progress in real-time with interactive charts and test results that update live as your model improves. Cloud training lets you get results quickly, without slowing down your computer.
  • Ship - Export your trained model to TensorFlow or CoreML and run it directly in your app on iOS and Android. Or use the easy-to-use Lobe Developer API and run your model remotely over the air.

On the other hand, DataRobot provides the following key features:

  • Automated machine learning
  • Data accuracy
  • Speed

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Detailed Comparison

DataRobot
DataRobot
Lobe
Lobe

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.

An easy-to-use visual tool that lets you build custom deep learning models, quickly train them, and ship them directly in your app without writing any code.

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
Build - Drag in your training data and Lobe automatically builds you a custom deep learning model. Then refine your model by adjusting settings and connecting pre-trained building blocks.; Train - Monitor training progress in real-time with interactive charts and test results that update live as your model improves. Cloud training lets you get results quickly, without slowing down your computer.; Ship - Export your trained model to TensorFlow or CoreML and run it directly in your app on iOS and Android. Or use the easy-to-use Lobe Developer API and run your model remotely over the air.
Statistics
Stacks
27
Stacks
1
Followers
83
Followers
18
Votes
0
Votes
0
Integrations
Tableau
Tableau
Domino
Domino
Looker
Looker
Trifacta
Trifacta
Cloudera Enterprise
Cloudera Enterprise
Snowflake
Snowflake
Qlik Sense
Qlik Sense
AWS CloudHSM
AWS CloudHSM
TensorFlow
TensorFlow
Keras
Keras

What are some alternatives to DataRobot, Lobe?

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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