Find Out Why Companies are Choosing Qeexo AutoML

Why AutoML Introduction

From Data to Actionable Insights

Billions of sensors are collecting data on every device imaginable.  However, up to 95.5% of the data collected is left unused.  Lacking sufficient machine learning expertise and resources, companies simply do not know how to leverage this data.  Qeexo, through its AutoML platform, addresses these challenges giving more companies than ever access to high-performance, lightweight machine learning models.

Qeexo AutoML Addresses

Key Challenges at the Edge.

Problem: Machine Learning is Difficult and Requires “Rocket Scientists”

 

Existing machine learning tools and platforms require deep technical knowledge to achieve commercial performance. There is a shortage of machine learning experts who can effectively bridge the gap from the lab to the real world.

 

Solution: Qeexo AutoML

 

Qeexo AutoML greatly simplifies the machine learning model building process, with its one-click, fully automated workflow. Many of the complicated tasks including data preprocessing, feature extraction and selection, model building and testing, hyperparameter optimization, and target deployment are automated using Qeexo AutoML. By reducing the complexity of building machine learning models, Qeexo AutoML can help companies make sense of their data without the need to invest in building expensive in-house machine learning teams, resulting in huge time and cost savings.

Why AutoML

Problem: Machine learning is labor-Intensive and time consuming.

 

Due to inefficient tools and the nature of machine learning for Edge devices, even experts take a long time to optimize machine learning models that need to function in the field.

 

Solution: Qeexo AutoML

 

AutoML is designed to automate many of the most tedious and time-consuming tasks in the ML building process, thereby also eliminating room for error. Using AutoML’s fully automated workflow, data scientists can dramatically reduce the pain and effort required to build models. AutoML gives companies the ability to maximize the efficiency of their existing data science teams, freeing them from work they hate, and deploying them to the areas they are needed most.

Why AutoML

Problem: Highly Constrained Environment Pose Additional Challenges

 

Machine learning at the Edge is subject to highly-constrained environments (processing power, memory size, and battery life). Developing models optimized to run in these conditions is incredibly challenging. In addition, sensor data common at the Edge is often unintuitive and requires signal processing expertise to understand (unlike, e.g. image data). Combined, these challenges result in sensor data not being fully utilized.

 

Solution: Qeexo AutoML

 

Qeexo has spent years developing machine learning solutions for constrained environments using sensor data. Qeexo AutoML allows anyone to leverage our experience and expertise to build machine learning models designed to deliver high-performance at the Edge, with ultra-low latency, memory requirement, and power consumption.

Why AutoML

Features


Running Edge

Optimized for Edge environments

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

Intuitive

Intuitive user experience

One-click UI, simple for anyone to use; no coding necessary.

Specialized Sensor Data

Leverages sensor data

Performs sensor fusion and is sensor agnostic.

Automated Feature Selection

Automated feature selection and extraction

Automatically generates and weights features to achieve the best performance with your data.

Machine Learning Models

Compare multiple machine learning models

View detailed metrics of a range of machine learning models to select the one that best fits your problem.

Data Visualization

Out-of-the-box data visualization

Visualize collected or uploaded data to understand patterns and potential problems.

On Demand Performance

On-demand Performance Evaluation

Provides model performance summaries, visualizations, and recommendations for improvements.

Easy Deployment

Easy deployment

Automatically translates models into C code to deploy to target hardware.

How can AutoML help your Organization?

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