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

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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Drag-and-Drop is Not Enough!

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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BigML Education videos

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

Orange

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Orange Data Mining videos

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.