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How to Bridge the Gaps Between Designing a Model and Deploying a Model on Edge Devices

Challenges of getting deep learning into production

While deep learning has vast potential for creating business success in various industries, it is also important to have a practical strategy to implement AI. According to many surveys, there exists a considerable gap between designing an artificial intelligence model and deploying one. Andrew Ng, a professor from Stanford University, indicates that a massive amount of engineering is needed to deploy the AI model in production. He further reveals that it is usual for teams to have another 12 to 24 months of work before the system can be deployed (source). This lengthy development cycle delays commercial success.

What causes the development cycle to be lengthy? 

Lack of hardware architecture knowledge and integration know-how are the main reasons. Data scientists mainly focus on building deep learning models and designing the algorithms within an AI team. They are experts in enhancing the AI model performance and deliver high-quality predictive results. However, most of the data scientists are not familiar with the hardware system knowledge, like how to conduct model inference on a specific type of edge device or graphic processing units (GPUs). Without the expertise in software and hardware integration and acceleration, it usually takes the company several months to figure out the way to deploy the models systematically.

To be more specific, the challenges of deploying deep learning models include the following: 

1. Model compression 

Given the limited computing resources on the edge device, only tiny models can be run on the edge devices. After developing an accurate model, the AI team should also compress it. Nonetheless, It is difficult to compress the model into a tiny size while maintaining the accuracy of predictive results, and thereby lengthening the development time.

2. Model conversion

To deploy the model and run the inference faster, the AI team should convert the deep learning model to a proper format. However, model conversion is complicated. To make the model applicable to run on different edge devices, the team should use different software development kits (SDK) respectively. For instance, to run model inference on a GPU provided by Qualcomm, the team must transfer the model into a deep learning container (DLC). But to run a model on the GPU produced by Nvidia, the team should apply TensorRT, a library developed by Nvidia for faster inference on NVIDIA GPUs.

Model compression and model conversion increase the time needed for launching an AI-based service, and thereby delays commercial success. To accelerate the AI deployment, companies should take action for change. 

How to Bridge the Gap?

Introducing artificial intelligence development tools can bridge the gaps between designing a model and deploying a model on edge devices. An effective artificial intelligence development tool provides a simple way to deploy the model and accelerate the development. For instance, FAST-AI, an artificial intelligence tool, provides several built-in functions, which allows the team to compress the model and convert the model into different formats in a short period of time. FAST-AI also significantly speeds up the process of model inference, making real-time prediction possible. 

By adopting artificial intelligence tools like FAST-AI, the AI project team is fully supported. Data scientists can focus on designing the model, instead of dealing with lots of hardware issues. In this way, companies can launch AI projects within a shortened period and achieve business success more quickly. 

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