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?

DataRobot is a leading automated machine learning platform that helps users build, deploy, and manage machine learning models. Its key features include automated feature engineering, model validation, and deployment, as well as time-series forecasting capabilities. However, some limitations of DataRobot include its high cost for small businesses, limited customization options, and the need for some level of technical expertise to fully utilize its capabilities.

  1. H2O.ai: H2O.ai offers open-source machine learning platforms that provide tools for building, training, and deploying machine learning models. Key features include automatic machine learning, interpretability, and scalability. Pros include open-source nature and easy integration with popular programming languages, while cons may include a steeper learning curve for beginners.
  2. Databricks: Databricks offers a unified analytics platform that combines data engineering, data science, and machine learning capabilities. Key features include collaborative workspace, automated machine learning, and integration with Apache Spark. Pros include seamless integration with cloud services and scalability, while cons may include cost implications for large-scale usage.
  3. Alteryx: Alteryx is a data science and analytics platform that provides tools for data preparation, blending, and modeling. Key features include workflow automation, predictive analytics, and drag-and-drop interface. Pros include ease of use and flexibility in data processing, while cons may include limited advanced machine learning capabilities.
  4. RapidMiner: RapidMiner is an integrated data science platform that offers tools for data prep, ML model building, and deployment. Key features include visual workflow design, automated machine learning, and model validation. Pros include user-friendly interface and extensive library of machine learning algorithms, while cons may include limitations in scalability for large datasets.
  5. IBM Watson Studio: IBM Watson Studio is a data science platform that provides tools for building and deploying AI models. Key features include autoAI, model management, and collaboration capabilities. Pros include integration with IBM Cloud services and enterprise-level support, while cons may include cost implications for small businesses.
  6. KNIME: KNIME is an open-source data analytics platform that offers tools for data blending, analysis, and machine learning. Key features include visual workflow design, integration with popular ML libraries, and community extensions. Pros include open-source nature and flexibility in customizing workflows, while cons may include limited support compared to commercial platforms.
  7. SAS Viya: SAS Viya is a cloud-native AI and analytics platform that provides tools for data preparation, model development, and deployment. Key features include automated machine learning, model interpretability, and scalability. Pros include integration with SAS analytics ecosystem and reliability in enterprise settings, while cons may include cost considerations for smaller organizations.
  8. Amazon SageMaker: Amazon SageMaker is a fully managed machine learning service that offers tools for building, training, and deploying ML models. Key features include built-in algorithms, model optimization, and scalable infrastructure. Pros include seamless integration with AWS services and pay-as-you-go pricing, while cons may include complexity in setting up and managing machine learning workflows.
  9. Google Cloud AutoML: Google Cloud AutoML is a suite of machine learning products that provide tools for custom model training and deployment. Key features include image recognition, natural language processing, and tabular data analysis. Pros include integration with Google Cloud services and user-friendly interface, while cons may include limited customization options compared to other platforms.
  10. Dataiku: Dataiku is a collaborative data science platform that offers tools for data preparation, visualization, and machine learning. Key features include visual flow-based interface, autoML capabilities, and collaboration tools. Pros include ease of use and collaboration features, while cons may include cost considerations for smaller teams.

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. ...

  • Postman
    Postman

    It is the only complete API development environment, used by nearly five million developers and more than 100,000 companies worldwide. ...

  • Postman
    Postman

    It is the only complete API development environment, used by nearly five million developers and more than 100,000 companies worldwide. ...

  • Stack Overflow
    Stack Overflow

    Stack Overflow is a question and answer site for professional and enthusiast programmers. It's built and run by you as part of the Stack Exchange network of Q&A sites. With your help, we're working together to build a library of detailed answers to every question about programming. ...

DataRobot alternatives & related posts

H2O logo

H2O

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H2O.ai AI for Business Transformation
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Databricks logo

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A unified analytics platform, powered by Apache Spark
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PROS OF DATABRICKS
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    Security
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    Data stays in your cloud account
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    Jan Vlnas
    Senior Software Engineer at Mews · | 5 upvotes · 455.4K views

    From my point of view, both OpenRefine and Apache Hive serve completely different purposes. OpenRefine is intended for interactive cleaning of messy data locally. You could work with their libraries to use some of OpenRefine features as part of your data pipeline (there are pointers in FAQ), but OpenRefine in general is intended for a single-user local operation.

    I can't recommend a particular alternative without better understanding of your use case. But if you are looking for an interactive tool to work with big data at scale, take a look at notebook environments like Jupyter, Databricks, or Deepnote. If you are building a data processing pipeline, consider also Apache Spark.

    Edit: Fixed references from Hadoop to Hive, which is actually closer to Spark.

    See more
    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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      Ease of use, great REST API and ML workflow automation
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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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          SAS logo

          SAS

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          A command-driven software package used for statistical analysis and data visualization
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              Postman logo

              Postman

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                Documentation
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              Engineering Manager at Segment · | 30 upvotes · 3M views

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              Postman is an “API development environment”. You download the desktop app, and build API requests by URL and payload. Over time you can build up a set of requests and organize them into a “Postman Collection”. You can generalize a collection with “collection variables”. This allows you to parameterize things like username, password and workspace_name so a user can fill their own values in before making an API call. This makes it possible to use Postman for one-off API tasks instead of writing code.

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              This turns Postman from a personal #API utility to full-blown public interactive API documentation. The result is a great looking web page with all the API calls, docs and sample requests and responses in one place. Check out the results here.

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              Simon Reymann
              Senior Fullstack Developer at QUANTUSflow Software GmbH · | 27 upvotes · 5.1M views

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              • A language along the entire stack: Widely used frameworks such as React or AngularJS or Vue.js, which we prefer, are written in JavaScript/TypeScript. If Node.js is now used on the server side, you can use all the advantages of a uniform script language throughout the entire application development. The same language in the back- and frontend simplifies the maintenance of the application and also the coordination within the development team.
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                The best api workflow out there
              • 53
                It's the best
              • 53
                History feature
              • 44
                Adds real value to my workflow
              • 43
                Great interface that magically predicts your needs
              • 35
                The best in class app
              • 12
                Can save and share script
              • 10
                Fully featured without looking cluttered
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                Collections
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                Option to run scrips
              • 8
                Global/Environment Variables
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                Shareable Collections
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                Dead simple and useful. Excellent
              • 7
                Dark theme easy on the eyes
              • 6
                Awesome customer support
              • 6
                Great integration with newman
              • 5
                Documentation
              • 5
                Simple
              • 5
                The test script is useful
              • 4
                Saves responses
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                This has simplified my testing significantly
              • 4
                Makes testing API's as easy as 1,2,3
              • 4
                Easy as pie
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                API-network
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                I'd recommend it to everyone who works with apis
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                Mocking API calls with predefined response
              • 2
                Now supports GraphQL
              • 2
                Postman Runner CI Integration
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                Easy to setup, test and provides test storage
              • 2
                Continuous integration using newman
              • 2
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              • 2
                Runner
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                Graph
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                <a href="http://fixbit.com/">useful tool</a>
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                Bloated features and UI
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                Poor GraphQL support
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                Expensive
              • 3
                Not free after 5 users
              • 3
                Can't prompt for per-request variables
              • 1
                Import swagger
              • 1
                Support websocket
              • 1
                Import curl

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              Noah Zoschke
              Engineering Manager at Segment · | 30 upvotes · 3M views

              We just launched the Segment Config API (try it out for yourself here) — a set of public REST APIs that enable you to manage your Segment configuration. A public API is only as good as its #documentation. For the API reference doc we are using Postman.

              Postman is an “API development environment”. You download the desktop app, and build API requests by URL and payload. Over time you can build up a set of requests and organize them into a “Postman Collection”. You can generalize a collection with “collection variables”. This allows you to parameterize things like username, password and workspace_name so a user can fill their own values in before making an API call. This makes it possible to use Postman for one-off API tasks instead of writing code.

              Then you can add Markdown content to the entire collection, a folder of related methods, and/or every API method to explain how the APIs work. You can publish a collection and easily share it with a URL.

              This turns Postman from a personal #API utility to full-blown public interactive API documentation. The result is a great looking web page with all the API calls, docs and sample requests and responses in one place. Check out the results here.

              Postman’s powers don’t end here. You can automate Postman with “test scripts” and have it periodically run a collection scripts as “monitors”. We now have #QA around all the APIs in public docs to make sure they are always correct

              Along the way we tried other techniques for documenting APIs like ReadMe.io or Swagger UI. These required a lot of effort to customize.

              Writing and maintaining a Postman collection takes some work, but the resulting documentation site, interactivity and API testing tools are well worth it.

              See more
              Simon Reymann
              Senior Fullstack Developer at QUANTUSflow Software GmbH · | 27 upvotes · 5.1M views

              Our whole Node.js backend stack consists of the following tools:

              • Lerna as a tool for multi package and multi repository management
              • npm as package manager
              • NestJS as Node.js framework
              • TypeScript as programming language
              • ExpressJS as web server
              • Swagger UI for visualizing and interacting with the API’s resources
              • Postman as a tool for API development
              • TypeORM as object relational mapping layer
              • JSON Web Token for access token management

              The main reason we have chosen Node.js over PHP is related to the following artifacts:

              • Made for the web and widely in use: Node.js is a software platform for developing server-side network services. Well-known projects that rely on Node.js include the blogging software Ghost, the project management tool Trello and the operating system WebOS. Node.js requires the JavaScript runtime environment V8, which was specially developed by Google for the popular Chrome browser. This guarantees a very resource-saving architecture, which qualifies Node.js especially for the operation of a web server. Ryan Dahl, the developer of Node.js, released the first stable version on May 27, 2009. He developed Node.js out of dissatisfaction with the possibilities that JavaScript offered at the time. The basic functionality of Node.js has been mapped with JavaScript since the first version, which can be expanded with a large number of different modules. The current package managers (npm or Yarn) for Node.js know more than 1,000,000 of these modules.
              • Fast server-side solutions: Node.js adopts the JavaScript "event-loop" to create non-blocking I/O applications that conveniently serve simultaneous events. With the standard available asynchronous processing within JavaScript/TypeScript, highly scalable, server-side solutions can be realized. The efficient use of the CPU and the RAM is maximized and more simultaneous requests can be processed than with conventional multi-thread servers.
              • A language along the entire stack: Widely used frameworks such as React or AngularJS or Vue.js, which we prefer, are written in JavaScript/TypeScript. If Node.js is now used on the server side, you can use all the advantages of a uniform script language throughout the entire application development. The same language in the back- and frontend simplifies the maintenance of the application and also the coordination within the development team.
              • Flexibility: Node.js sets very few strict dependencies, rules and guidelines and thus grants a high degree of flexibility in application development. There are no strict conventions so that the appropriate architecture, design structures, modules and features can be freely selected for the development.
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              Stack Overflow logo

              Stack Overflow

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              61K
              893
              Question and answer site for professional and enthusiast programmers
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                Voting system
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                Good questions
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                Addictive
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                Tight focus
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              Tom Klein

              Google Analytics is a great tool to analyze your traffic. To debug our software and ask questions, we love to use Postman and Stack Overflow. Google Drive helps our team to share documents. We're able to build our great products through the APIs by Google Maps, CloudFlare, Stripe, PayPal, Twilio, Let's Encrypt, and TensorFlow.

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