Alternatives to DataRobot logo

Alternatives to DataRobot

H2O, Databricks, BigML, RapidMiner, and SAS are the most popular alternatives and competitors to DataRobot.
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What is DataRobot and what are its top alternatives?

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.
DataRobot is a tool in the Machine Learning Tools category of a tech stack.
DataRobot is an open source tool with GitHub stars and GitHub forks. Here’s a link to DataRobot's open source repository on GitHub

Top Alternatives to DataRobot

  • 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. ...

  • Databricks

    Databricks

    Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications. ...

  • BigML

    BigML

    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. ...

  • RapidMiner

    RapidMiner

    It is a software platform for data science teams that unites data prep, machine learning, and predictive model deployment. ...

  • SAS

    SAS

    It is a command-driven software package used for statistical analysis and data visualization. It is available only for Windows operating systems. It is arguably one of the most widely used statistical software packages in both industry and academia. ...

  • 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. ...

  • 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/ ...

DataRobot alternatives & related posts

H2O logo

H2O

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160
4
H2O.ai AI for Business Transformation
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160
+ 1
4
PROS OF H2O
  • 1
    Highly customizable
  • 1
    Very fast and powerful
  • 1
    Auto ML is amazing
  • 1
    Super easy to use
CONS OF H2O
  • 1
    Not very popular

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Databricks logo

Databricks

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A unified analytics platform, powered by Apache Spark
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PROS OF DATABRICKS
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      BigML logo

      BigML

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      Machine Learning, made simple. Predictive analytics for big data and not-so-big data.
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      PROS OF BIGML
      • 1
        Ease of use, great REST API and ML workflow automation
      CONS OF BIGML
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        RapidMiner logo

        RapidMiner

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        Prep data, create predictive models & operationalize analytics within any business process
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        + 1
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        PROS OF RAPIDMINER
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          CONS OF RAPIDMINER
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            SAS logo

            SAS

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            A command-driven software package used for statistical analysis and data visualization
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            PROS OF SAS
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              CONS OF SAS
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                TensorFlow logo

                TensorFlow

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                Open Source Software Library for Machine Intelligence
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                PROS OF TENSORFLOW
                • 24
                  High Performance
                • 16
                  Connect Research and Production
                • 13
                  Deep Flexibility
                • 9
                  Auto-Differentiation
                • 9
                  True Portability
                • 2
                  Easy to use
                • 2
                  High level abstraction
                • 1
                  Powerful
                CONS OF TENSORFLOW
                • 8
                  Hard
                • 5
                  Hard to debug
                • 1
                  Documentation not very helpful

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                Conor Myhrvold
                Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.2M views

                Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

                At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

                TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

                Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

                https://eng.uber.com/horovod/

                (Direct GitHub repo: https://github.com/uber/horovod)

                See more

                In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.

                Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

                !

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                PyTorch logo

                PyTorch

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                A deep learning framework that puts Python first
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                PROS OF PYTORCH
                • 11
                  Easy to use
                • 9
                  Developer Friendly
                • 8
                  Easy to debug
                • 5
                  Sometimes faster than TensorFlow
                CONS OF PYTORCH
                • 2
                  Lots of code

                related PyTorch posts

                Server side

                We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.

                • Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.

                • Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.

                • Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.

                Client side

                • UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.

                • State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.

                • Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.

                Cache

                • Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.

                Database

                • Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.

                Infrastructure

                • Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.

                Other Tools

                • Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.

                • Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.

                See more
                Conor Myhrvold
                Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.2M views

                Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

                At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

                TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

                Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

                https://eng.uber.com/horovod/

                (Direct GitHub repo: https://github.com/uber/horovod)

                See more
                Keras logo

                Keras

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                907
                12
                Deep Learning library for Theano and TensorFlow
                871
                907
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                12
                PROS OF KERAS
                • 5
                  Quality Documentation
                • 4
                  Easy and fast NN prototyping
                • 3
                  Supports Tensorflow and Theano backends
                CONS OF KERAS
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                Conor Myhrvold
                Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.2M views

                Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

                At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

                TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

                Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

                https://eng.uber.com/horovod/

                (Direct GitHub repo: https://github.com/uber/horovod)

                See more

                I am going to send my website to a Venture Capitalist for inspection. If I succeed, I will get funding for my StartUp! This website is based on Django and Uses Keras and TensorFlow model to predict medical imaging. Should I use Heroku or PythonAnywhere to deploy my website ?? Best Regards, Adarsh.

                See more