The business value of accelerating machine learning predictions

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Machine learning models are built so that they can run predictions. Unlike the training stage which takes known inputs and trains the model to generate the desired result, predictions are when the model takes new inputs and generates an outcome.

Making models run faster sounds nice, but is there business value to be had in accelerating predictions?

Yes, and this business value typically falls into one of two buckets:

    1. Faster response time. A model that generates the results faster, without losing accuracy, can be valuable in time-sensitive applications. For instance, if an autonomous driving model is faster, it can react faster to pedestrians or obstacles. If a natural language processing model runs faster, the interaction of a human user with such a model will be smoother and more natural. 

    2. Reduced costs. When models execute in the cloud, the compute cost is proportional to the execution time. Faster models can result in significant cost savings. When a model executes on the edge, a faster and optimized model can allow companies to use a lower-cost processing unit, or to add useful functionality into existing ones.

Both faster response times and reduced costs can translate into competitive advantages, hence generating business value.

Inteon is building the technology to exploit untapped potential in the hardware that supports deep learning inference deployed at scale. Contact us today to learn how we can help you improve response times and reduce costs to improve your business outcomes.