Qeexo AutoML

Qeexo

TRIAL

Machine Learning Platform

Discover what you can achieve with Qeexo AutoML

GBM XGBoost Isolation Forest Random Forest Decision Tree SVM Local Outlier Factor Logistic Regression ANN CNN RNN CRNN
  • Machine Learning that
    Runs on a Cortex-M0!
    Smaller, faster, better

    Machine learning models built with Qeexo AutoML are highly optimized and have an incredibly small memory footprint. Models are designed to run locally on embedded devices (as small as a Cortex-M0!) – ideal for ultra low-power, low-latency applications on MCUs and other highly constrained platforms.

  • Automatically Build
    ML Solutions
    Faster and easier than ever before

    Unlike other fragmented machine learning tools and frameworks that require expert engineers to cobble together, Qeexo AutoML walks users through the entire machine learning development process, all from within our intuitive UI – no coding necessary!

Qeexo AutoML Features

  • Optimized for the Edge

    Supports Arm® Cortex®-M0-to-M4 class MCUs and other constrained environments

  • Leverages sensor data

    Ingests data from multiple streams (sensor fusion) and is sensor agnostic

  • Wide range of ML methods

    Compares results from many algorithms: regressors, decision trees & neural nets

  • Automated feature extraction

    Generates and weights features from your data for the best performance

  • Intuitive user experience

    Click-through UI with no coding required

  • Data visualization

    Visualize collected or uploaded data to understand patterns and problems

  • Performance reporting

    Provides model performance summaries, visualizations, and recommendations

  • Easy deployment

    Translates models into C to compile and deploy to target embedded hardware

  • Custom hardware integration

    Option to add Qeexo AutoML support to your specific hardware

Qeexo AutoML is the solution
to common machine learning challenges

Challenges

  • 1. Severe shortage of skilled resources
  • 2. Machine learning is labor-intensive and time-consuming
  • 3. Highly constrained environments are difficult to work with

Solutions

  • 1. Replaces the need for disparate experts
  • 2. Automates many of the most tedious steps and putting in guardrails
  • 3. Translates models to C for deployment to embedded devices

Benefits

  • 1. Maximizes the efficiency of data science teams
  • 2. Eliminates human-induced errors and significantly shortens time required
  • 3. Builds machine learning models optimized for the Edge
Try Qeexo AutoML

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