Graphical Tools for Modeling AI Components
- Analytics ... Visualization ... Graphical Tools ... Diagrams & Business Analysis ... Requirements ... Loop ... Bayes ... Network Pattern
- Development ... Notebooks ... AI Pair Programming ... Codeless, Generators, Drag n' Drop ... AIOps/MLOps ... AIaaS/MLaaS
- Neural Network Zoo | Fjodor Van Veen
- 10 Tools for Modeling AI Components – Machine Learning without the code | Jordi Cabot - Modeling Languages
- 19 Data Science and Machine Learning Tools for people who Don’t Know Programming | Aarshay Jain - Analytics Vidhya
- Artificial General Intelligence (AGI) to Singularity ... Curious Reasoning ... Emergence ... Moonshots ... Explainable AI ... Automated Learning
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
Generic data analytics platform that can be used for a multitude of tasks. KNIME comes with over 2000 different types of nodes
Teach your app to see emotions. Build, train, and ship custom deep learning models using a simple visual interface.
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.
Claims to have obviated the need for data scientists. Python SDK and APIs available for quick integration of models into tools and software.
Monthly, quarterly and yearly subscriptions.
- Deep Learning made easy with Deep Cognition | Favio Vázquez - Towards Data Science - Medium
- A video walkthrough of Deep Cognition | Favio Vázquez - Towards Data Science - Medium
- Towards an Easier Deep Learning Life with Deep Cognition | Favio Vázquez - Medium
Drag and drop neural networks and create Deep Learning models with Automated Learning
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.
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.
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.
Builds on top of TensorBoard to visualize deep neural networks