We can reducing time taken and resources required to arrive at meaningful answers from deep learning. Finding the specific combination of data, deep algorithms, software and hardware that delivers the outputs you want, without time getting away or breaking the bank or requiring rocket science, is an art in itself. Understanding and optimizing the technical environment in which deep learning is performed is critical to delivering bang for buck.
Doing deep learning – better, can take many forms depending on your requirements. Some examples of the kinds of things that we can do to help include:
- Reduce the training time for deep learning models (while balancing results), so that they can be deployed faster
- Reduce the size of deep learning models (while balancing results), so they can be used more widely
- Enable and optimise deep learning models to be deployed on specific hardware/devices and take maximum advantage of its capabilities