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

DataRobot vs baikal

OverviewComparisonAlternatives

Overview

DataRobot
DataRobot
Stacks27
Followers83
Votes0
baikal
baikal
Stacks4
Followers11
Votes0
GitHub Stars590
Forks30

DataRobot vs baikal: What are the differences?

What is 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.

What is baikal? A graph-based functional API for building complex scikit-learn pipelines. It is a graph-based, functional API for building complex machine learning pipelines of objects that implement the scikit-learn API. It is mostly inspired on the excellent Keras API for Deep Learning, and borrows a few concepts from the TensorFlow framework and the (perhaps lesser known) graphkit package. It aims to provide an API that allows to build complex, non-linear machine learning pipelines.

DataRobot and baikal can be categorized as "Machine Learning" tools.

Some of the features offered by DataRobot are:

  • Automated machine learning
  • Data accuracy
  • Speed

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

  • Build non-linear pipelines effortlessly
  • Handle multiple inputs and outputs
  • Add steps that operate on targets as part of the pipeline

baikal is an open source tool with 553 GitHub stars and 23 GitHub forks. Here's a link to baikal's open source repository on GitHub.

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Detailed Comparison

DataRobot
DataRobot
baikal
baikal

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.

It is a graph-based, functional API for building complex machine learning pipelines of objects that implement the scikit-learn API. It is mostly inspired on the excellent Keras API for Deep Learning, and borrows a few concepts from the TensorFlow framework and the (perhaps lesser known) graphkit package. It aims to provide an API that allows to build complex, non-linear machine learning pipelines.

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 non-linear pipelines effortlessly; Handle multiple inputs and outputs; Add steps that operate on targets as part of the pipeline; Nest pipelines; Use prediction probabilities (or any other kind of output) as inputs to other steps in the pipeline; Query intermediate outputs, easing debugging; Freeze steps that do not require fitting; Define and add custom steps easily; Plot pipelines
Statistics
GitHub Stars
-
GitHub Stars
590
GitHub Forks
-
GitHub Forks
30
Stacks
27
Stacks
4
Followers
83
Followers
11
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
Python
Python
scikit-learn
scikit-learn

What are some alternatives to DataRobot, baikal?

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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