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. However, the lengthy development cycle prevents deep learning projects from being applied to the market. 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.
|This blog addresses the following questions|
- Why is the AI development cycle lengthy?
- Why is it hard to shorten the period of model training?
- How to reduce the development time with artificial intelligence tools?
- Why is the AI development cycle lengthy?
One of the time-consuming parts of an artificial intelligence project is model training. 76% of data scientists have the same pain point that they spend most of their working time waiting for computing at the stage of building and training models. For data scientists, it is distressful to wait for a long time, especially at the stage of testing and conducting hyperparameter optimization. They could wait for several hours but only figure out a few times whether the adjustment of hyperparameters increases the performance of the model.
Model Training
Why is model training so time-consuming?
The common reasons are as follow:
1. Environment settings are not optimized
Numerous environment settings affect the speed of model training. Practitioners have to spend a lot of time monitoring and analyzing performance profilers to diagnose the bottleneck. From environment settings to deep learning frameworks, the optimization of the environment reduces the time needed for model training.
2. Computing resources are limited
A low GPU utilization rate leads to the inefficiency of computing, and the insufficient GPU memory directs to the limited input of datasets. Sometimes, insufficient GPU resource allocation causes a longer time when sharing GPUs resources among multiple users within the data team.
How to reduce the development time of AI model training?
To solve the above issues and accelerate model training, several methods could be considered. For instance, training models in parallel or distribution with multiple GPUs allows a shorter time for model training. To improve the GPU utilization rate, data scientists could consider increasing the batch size and system throughput.
However, data scientists rarely apply those methods. In fact, to successfully accelerate model training, data scientists should spend lots of time studying lots of research papers and surveying technical guidelines provided by the GPU suppliers. As data scientists devote a majority of their time to enhance model performance, it is difficult for them to spend extra effort in investigating how to accelerate model training.
Under this circumstance, data scientists could consider using AI tools. The artificial intelligence tool, hAIsten, aims at providing an optimized environment for data scientists. hAIsten provides a platform for data scientists to train and deploy models, and it significantly decreases the time needed for model training. hAIsten optimizes the environment settings and builds distributed training as default. It supports different deep learning frameworks such as Tensorflow, Pytorch, as well as different types of GPU, from cloud to edge devices. In this way, the artificial intelligence software hAIsten accelerates the model training and decreases the workload for data scientists (Learn more about the artificial intelligence tools).
Model Deployment on Edge Device
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 AI software can bridge the gaps between designing a model and deploying a model on edge devices. An effective artificial intelligence software provides a simple way to deploy the model and accelerate the development. For instance, hAIsten, 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.
hAIsten also significantly speeds up the process of model inference, making real-time prediction possible. By adopting artificial intelligence tools, 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 (Learn more about how hAIsten accelerating model inference and deployment).