Deep learning has broad applications in sentiment analysis, natural language understanding, computer vision, etc. The technology is growing at a breakneck speed on the back of rapid innovation. However, such innovations call for a higher number of parameters and resources. In other words, the model is as good as the metrics.

To that end, Google researcher Gaurav Menghani has published a paper on model efficiency. The survey covers the landscape of model efficiency from modelling techniques to hardware support. He proposed a method to make ‘deep learning models smaller, faster, and better’.

Challenges

Menghani argues that while larger and more complicated models perform well on the tasks they are trained on, they may not show the same performance when applied to real-life situations.

Following are the challenges practitioners face while training and deploying models:

• The cost of training and deploying large deep learning models is high. The large models are memory-intensive and leave a bigger carbon footprint.
• A few deep learning applications need to run in real-time on IoT and smart devices. This calls for optimisation of models for specific devices.
• Building training models with as little data as possible when the user data might be sensitive.
• Off the shelf models may not always be able to address the constraints of new applications.
• Training and deployment of multiple models on the same infrastructure for different applications may exhaust available resources.

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