Kauricone Machine Learning

The Kauricone Machine Learning Process

Begin with Image Recognition

Kauricone are focussed on Image Recognition applications for Machine Learning. To do this we follow this process:

1. Collect more than 2000 images of the object to be detected
2. Annotate each image (ie put a border around each object to clearly identify it from any other object in the same image frame)
3. Identify as many traits of the object as possible
4. Begin training the machine with the objects, and traits that are available
5. Take images of the objects in the production environment
6. Process these images against the trained model at regular intervals
7. Look for exceptions and trends which require action

Make Predictions

This is the output from the server, after an image has been processed

Solution Requirements

1. Kauricone TinyML Server (4GB, 128 eMMC Storage,ARM 6 Core Processor, Connection Interfaces)
2. Camera (Wifi, 4G, Network, USB)
3. Computer
4. Ubuntu 18.04, Tensorflow, CNN (Convolutional Neural Network), Python (All Preinstalled)

Kauricone Machine Learning Pricing

 

Monthly Subscription $250 (Includes 1,2, and 4 of the Requirements above)

Implementation $200 per hour

Support $190 per month (Optional)

Hardware Maintenance $50 per month (Optional)

 

What is Machine Learning?

Machine Learning uses technology to collect data and predict the future to help business make decisions

Machine Learning analyses the data collected by applying statistical analysis, and pattern matching, to learn from past experiences. Using the trained data, it provides predicted results

Some Advantages of Machine Learning

Easy to identify Trends and Patterns

Machine Learning can review large volumes of data and identify specific patterns and trends that may not be apparent to humans

Automation

The objective of machine learning is that the machine has the ability to learn and make predictions, and improve accuracy as time goes by. This removes much of the labour cost associated with gathering information in the traditional way

Continuous Improvement

The accuracy and efficiency of the machine improves rapidly over time. This means better decisions can be made with more up to date and accurate data

Some Disadvantages of Machine Learning

Collecting Training Data

Machine Learning requires a very large amount of data to train on for more precision.

 

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