Cases

Image Recognition

Label an image set to indicate what objects are in the images

Use 75% of the images for the machine learning phase using different methods (decision tree, nearest neighbour

Use 25% of the data to evaluate the machine learning models

Use the model to identify objects on the images

Tools used : Python Imaging Library, python Matplotlib ,python scikit-image, PyTorch

Service Level Management

Label a dataset to indicate which service was good or bad based on Service Level Agreement parameters • Use 75% of the data for the machine learning phase using different methods (decision tree, nearest neighbour) • Use 25% of the data to evaluate the machine learning models • Use the model to predict quality of service and improve it • Tools used: python – pandas, python scikit_learn, python Matplotlib, python networkx

Manufacturing process optimization

Based on input from the manufacturing process (data) our machines can predict the output or the quality.
These emulations of your production process can be used to optimize the process to reduce waste and increase yield.
A presentation describing this can be downloaded here