SmartPredict
  • Documentation
  • OVERVIEW
    • Presentation
      • Key features
      • Who may benefit from its use
      • The SmartPredict Graphical User Interface (GUI)
        • The SmartPredict Modules
        • Focus on the Notebooks
        • Practical shortcuts
    • Prerequisites
  • Getting started
    • Getting started (Part1) : Iris classification project
      • Project description
      • Step 1. Create the project
      • Step 2. Upload the dataset
      • Step 3. Preprocess the dataset
      • Step 4. Build the flowchart
        • Set up the flowchart
        • Configure the modules
      • Step 5. Run the build
      • Step 6. Deploy the project
      • Step 7. Make inferences with our model
      • Conclusion
  • Getting started (Part 2): Predicting the passengers' survival in the Titanic shipwreck
    • Project description
    • Step 1. Create the project
    • Step 2. Upload the dataset
    • Step 3. Preprocess the dataset
    • Step 4. Build the flowchart
    • Step 5. Run the build
    • Step 6. Deploy the pipeline
    • Step 7. Make inferences with our pipeline
  • MODULE REFERENCE
    • CORE MODULES
      • Introduction
      • Basic Operations
        • Item Saver
      • Web Services
        • Web Service IN and OUT
      • Data retrieval
        • Data fetcher
        • Data frame loader/converter
        • Image data loader
      • Data preprocessing
        • Introduction
        • Array Reshaper
        • Generic Data Preprocessor
        • Missing data handler
        • Normalizer
        • One Hot Encoder
        • Ordinal Encoder
      • Data selection
        • Features selector
        • Generic data splitter
        • Labeled data splitter
      • Training and Prediction
        • Predictor DL models
        • Predictor ML models
        • Predictor ML models (Probabilistic models)
        • Trainer ML models
        • Trainer/Evaluator DL models
      • Evaluation and fine-tuning
        • Cross Validator for ML
        • Evaluator for ML models
      • Machine Learning algorithms
        • ML modules in SmartPredict
        • Decision Tree Regressor
        • KNeighbors Classifier
        • KNeighbors Regressors
        • Linear Regressor
        • Logistic Regressor
        • MLP Regressor
        • Naive Bayes Classifier
        • Random Forest Classifier
        • Random Forest Regressor
        • Support Vector Classifier
        • Support Vector Regressor
        • XGBoost Classifier
        • XGBoost Regressor
      • Deep learning algorithms
        • Dense Neural Network
        • Recurrent Neural Networks
      • Computer Vision
        • Convolutional Recurrent Networks
        • Fully Convolutional Neural Networks
        • Face detector
        • Image IO
        • Image matcher
        • Yolo
      • Natural Language Processing
        • Introduction
        • Text cleaner
        • Text vectorizer
      • Times Series processing
        • TS features selector
      • TensorFlow API
        • LSTM Layer
        • Dense Layer
      • Helpers
        • Data/Object Logger
        • Object Selector (5 ports)
      • Conclusion
  • CUSTOM MODULES
    • Function
    • Class
    • Use cases
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  • Why SmartPredict?
  • ✨ SmartPredict offers 5 distinctive key features :

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  1. OVERVIEW

Presentation

As an introduction, we would like to walk you through SmartPredict Studio's distinctive features. Also, we would like to highlight how each professional category of users can get the most of it .

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Last updated 5 years ago

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Why SmartPredict?

SmartPredict Studio stands out for its unique value proposition made of a large palette of tools made of powerful algorithms, an option to create custom modules, a user-friendly interface,and its ergonomic modeling functions navigation elements and more.... All of which makes of it a smart tool.

The effortless handling ,displacement, and assembling of flowcharting components compose its best assets: the drag and drop mode makes them all so easy.

Apart from that, to cover the granularity and singularity of their projects, users also have the possibility of coding their own modules or snippets in Python language, and this, right from the SmartPredict Notebooks themselves.

Create and deploy your Machine Learning models seamlessly , all while exploring the modules' richness and taking advantage of the SmartPredict platform's robustness.

✨ SmartPredict offers 5 distinctive key features :

  1. A drag and drop workflow for model designing and for deployment pipeline. The AI Workflow is represented as flowcharts. Assembling modules for every stage is a matter of minutes, in drag and drop mode .

  2. SmartApps are add-on functionalities to enhance SmartPredict' s core programs. They are useful for the consistent integration of AI specific modeling purposes such as Data processing and Image labeling for instance . A SmartApp for Image Labeling :The image labeling SmartApp includes special functions for collaborative tagging and for attaching class or text to bounding boxes. Use the collection as a drag and drop module. A SmartApp for Data Processing and Visualization : With more than 100 processors, the SmartApp for Data Processing and Visualization is able to handle any data refining operations from simple to complex, like handling missing values, sorting and filtering, and exporting the processing pipelines, also as a drag and drop module.

  3. A Notebook for coding in SmartPredict comes with its unique Notebook interface for code-lovers allowing them to incorporate their favorite libraries into SmartPredict projects. Additional code snippets and custom modules can be embedded through Notebooks with the help of the Python language.

  4. An all-in-one online platform : Export Dataset and Pipelines from Dataset Processing, Export Image Dataset from Image labeling. Interact with your workspace using the SmartPredict API, accessible through the Notebook or externally.

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