Graphical Tools for Modeling AI Components

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These range from a new interface for a tool that completely automates the process of creating models, to a new no-code visual interface for building, training and deploying models, all the way to hosted Jupyter-style notebooks for advanced users. Tools that allow visual drag-and-drop interface to streamline and simplify your process

KNIME

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Generic data analytics platform that can be used for a multitude of tasks. KNIME comes with over 2000 different types of nodes

PerceptiLabs

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lobe

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Teach your app to see emotions. Build, train, and ship custom deep learning models using a simple visual interface.

RapidMiner

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Covers the entire life-cycle of prediction modeling, starting from data preparation to model building and finally validation and deployment. - includes over 60 file types and formats for structured and unstructured data, built-in templates and repeatable workflows. Custom R and Python scripts can be integrated into the system.

DataRobot (DR)

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Claims to have obviated the need for data scientists. Python SDK and APIs available for quick integration of models into tools and software.

BigML

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Monthly, quarterly and yearly subscriptions.

Deep Cognition Deep Learning Studio

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Drag and drop neural networks and create Deep Learning models with Automated Machine Learning (AML) - AutoML

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Orange

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Open source machine learning and data visualization toolkit. Data analysis is done by linking widgets in workflows. Each widget may embed some data retrieval, preprocessing, visualization, modeling or evaluation tasks. A considerable number of predefined widgets are available but you can also build your own.

Dataiku

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Integrates with a large number of other tools, from notebooks to chart libraries for data visualization and all major machine learning libraries. Once the pipeline is setup, you can bundle it as a single deployable package for real-time predictions via a REST API.

Dianne

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Neural networks are built as a directed graph. DIANNE comes with a web-based UI builder to drag-and-drop neural network modules and link them together.

TensorFlow

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NVIDIA Deep Learning GPU Training System (DIGITS)

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Builds on top of TensorBoard to visualize deep neural networks

H2O - Driverless AI

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For automated Visualization, feature engineering, model training, Hyperparameter optimization, and explainability.

Microsoft Azure - Azure Machine Learning studio

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IBM - Watson Studio

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