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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On this page
  • 1. All-in-one AI platform
  • 2. Drag and drop workspace
  • 3. SmartApps
  • -Image labeling
  • -Data Processing and Visualization
  • 4. Notebooks for project customization
  • 5. Integrations

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  1. OVERVIEW
  2. Presentation

Key features

This section is used to highlight and to describe SmartPredict' s key features.

PreviousPresentationNextWho may benefit from its use

Last updated 5 years ago

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1. All-in-one AI platform

SmartPredict is an all-in-one integrated AI platform especially aimed at Data scientists, Data engineers , Data analysts, Machine Learning engineers , AI practitioners, Academics and Software engineers. It has been designed to optimize the process of AI modeling in order to increase productivity, time gain and effectiveness.

For modeling AI projects, SP offers a comprehensive environment to complete a project integrally, right from the same place.

The distinct AI modeling stages : build, deploy, monitor, and test can all four be performed within SmartPredict' s generic platform.

In support to that , thanks to a rich palette of versatile modules or specialized ones, SP also covers a large number of use cases of various levels of complexity.

2. Drag and drop workspace

SmartPredict' s mode of assembling flowcharts is easy and intuitive. Simply drag and drop modules directly to the workspace to constitute an AI visual workflow.

In the same fashion, upload all kinds of functional items such as datasets, models and processing pipelines and transport them into your workspace through drag and drop.

3. SmartApps

SmartApps are add-on utilities to enhance SmartPredict' s core. You will find them practical for integration of specific modeling purposes.

For instance, for handling respectively the purposes of labeling image datasets and the tasks related to processing data of all sorts, SmartPredict possesses two special applications:

-Image labeling

"Image labeling" is a SmartApp which makes it easy to shape labeled image datasets for your image-based machine learning applications. Open the app, create your collection and begin attaching classes or text to bounding boxes. Use the collection as a drag and drop module.

-Data Processing and Visualization

"Data Processing and Visualization" is another SmartApp designed to help you get the most out of your data by exploring, processing and visualizing them.

Apart from profiling your data; this simple to use application also deals with the conception of processing pipelines . To do so, it contains an embedded powerful tool for handling missing data, for array reshaping, sorting and normalizing and even a one hot encoder:

Just like SP's other workspaces , it also comprises a drag and drop interface for producing insightful data visualizations.

4. Notebooks for project customization

SmartPredict contains a handy set of functions for personalizing your modules. They are gathered under the Notebook applications menu.

Thus, if a particular project requires you to code your own modules, do not ever feel limited: you will be able to do so through the Notebooks.

The Notebooks allow you to extend the capacities of your projects by coding and adding custom modules, user-defined functions and snippets .

5. Integrations

In addition to its inferential capacities with the REST API Web Services , SmartPredict also offers the possibility of connecting with version control repositories such as .

GitHub
A large palette of modules is one of SmartPredict' s main assets.
The drag and drop mode makes modeling with SmartPredict easy and intuitive.
Image labeling is as easy as capturing and tagging items in image datasets.
The Data Processor is able to perform cleansing operations.
Notebooks are handy tools for customizing modules and snippets.
SmartPredict is able to be integrated with GitHub.
SmartPredict is able to be integrated with Github.