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ingénierie des fonctionnalités

  • A no-code AI tool for creating machine learning models.
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    What is Graphite Note?
    Graphite Note is a no-code AI platform that helps businesses create machine learning models in minutes. It automates data pre-processing, feature engineering, and model selection, making predictive analytics accessible to users without technical expertise. The platform leverages Auto ML and data storytelling to convert raw data into key drivers and actionable insights, accelerating business growth.
    Graphite Note Core Features
    • Automated Machine Learning
    • Data Preprocessing
    • Feature Engineering
    • Model Selection
    • Hyperparameter Tuning
    • Model Evaluation
    • Actionable Insights
    Graphite Note Pro & Cons

    The Cons

    Not specified if the platform supports open source or community contributions.
    No visible public GitHub repository or open-source project available.
    No mobile app stores or extension links found, limiting accessibility outside web.
    Pricing details are only accessible by visiting a separate pricing page.

    The Pros

    No-code platform making machine learning accessible to non-technical users.
    Pre-built AI templates for various business use cases enabling quick deployment.
    Automated best model selection to save time and simplify model building.
    Explainable AI that provides insights into key drivers behind predictions.
    Supports actionable and prescriptive analytics to recommend next best actions.
    Enables real-time 'what-if' scenario analysis for faster decision making.
    Works with any tabular business data from spreadsheets, databases, or cloud sources.
    Graphite Note Pricing
    Has free planNo
    Free trial details14-day free trial for the Growth plan
    Pricing modelFree Trial
    Is credit card requiredNo
    Has lifetime planNo
    Billing frequencyMonthly

    Details of Pricing Plan

    Growth

    995 USD
    • Performance servers
    • Up to 10 models
    • 3 users included
    • 10M total dataset rows
    • CSV and database connectors
    • Basic Generative AI actionable insights
    • Advanced model parameters
    • API predictions and model results
    • Access to all autoML model types
    • Email, AI chat and calls support

    Enterprise

    Custom USD
    • High-performance servers
    • Custom number of models, users, and data rows
    • All data connectors
    • Unlimited predictions and notebooks
    • High-end Generative AI actionable insights
    • Advanced timeseries parameters
    • Full list of ML algorithms available
    • Highest API rate limits
    • Access to all autoML model types
    • Dedicated account manager priority support
    For the latest prices, please visit: https://graphite-note.com/no-code-machine-learning-pricing/
  • Timetk: Efficient time series analysis and forecasting tool.
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    What is TimeTK?
    Timetk provides a comprehensive suite of tools tailored for handling time series data. With its user-friendly interface, it simplifies tasks such as data visualization, feature engineering, and forecasting. Users can easily manipulate time-based indexes, making it particularly useful for data scientists and analysts engaged in predictive modeling. The package extends standard functionalities available in R, allowing for more seamless integration and functionality across various datasets. By offering these robust features, Timetk empowers users to extract insights and make informed predictions from complex time series data.
  • AutoML-Agent automates data preprocessing, feature engineering, model search, hyperparameter tuning, and deployment via LLM-driven workflows for streamlined ML pipelines.
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    What is AutoML-Agent?
    AutoML-Agent provides a versatile Python-based framework that orchestrates every stage of the machine learning lifecycle through an intelligent agent interface. Starting with automated data ingestion, it performs exploratory analysis, missing value handling, and feature engineering using configurable pipelines. Next, it conducts model architecture search and hyperparameter optimization powered by large language models to suggest optimal configurations. The agent then runs experiments in parallel, tracking metrics and visualizations to compare performance. Once the best model is identified, AutoML-Agent streamlines deployment by generating Docker containers or cloud-native artifacts compatible with common MLOps platforms. Users can further customize workflows via plugin modules and monitor model drift over time, ensuring robust, efficient, and reproducible AI solutions in production environments.
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