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What does a Deep Learning Engineer do?
Deep learning has transformed many industries, and deep learning engineers have become high-paying professionals. However, there is an increasing trend of competition. Previously, it was sufficient to know how to train models, but now most deep learning engineer positions require proficiency in both model training and deployment.It is no exaggeration to say that in the future, only deep learning engineers who can train, deploy, and are proficient in C++ for model deployment will continue on the path of high salaries. Otherwise, according to this competitive trend, deep learning engineers who can only train models without deployment will inevitably face challenges from an increasing number of developers, and their previously accumulated advantages will vanish. Therefore, mastering skills from data labeling to model training to model deployment is a basic requirement for deep learning engineers and an inevitable trend for the future.
Deep Learning Model Deployment Scenarios
The main scenarios for deep learning model deployment include:
01
Cloud Deployment Scenario
This is primarily based on cloud servers and distributed services. Enterprises need to pay for cloud server computing power and storage. The advantage is that it is easy to scale and convenient for rapid deployment of model algorithms at multiple locations and nodes; the disadvantage is that compared to edge deployment, it has higher latency, lower reliability, and data security challenges, not fully utilizing the computing power of edge devices.
02
Edge (PC-side) Deployment Scenario
This is an ideal choice for high-performance applications, highly customizable (built with application-specific components), and flexible pricing (as components can be selected based on applications). The advantage is that costs are controllable, data security is guaranteed, and it offers low latency and high reliability. Therefore, this solution is widely adopted in fields such as machine vision and security monitoring, relying on industrial computers and graphics cards to provide computing power for model deployment, supporting defect detection, security monitoring, and automated production. The disadvantage is that it is still not widely adopted in industries that are particularly sensitive to costs.
03
Edge (ARM, FPGA, and inference boards, smart cameras)
Edge and end-side deployment is an important scenario, typically involving various AI boxes, including Intel’s NUC boxes, the newly launched AlxBorad boards, NVIDIA’s Jetson series boards, RK series boards, Raspberry Pi, etc., which have been used to implement various smart devices. Their advantages include low cost, low power consumption, significant savings on peripheral hardware computing power, while ensuring high reliability and security, suitable for scenarios with less stringent computing power requirements, supporting various lightweight model deployments. The disadvantage is that it requires a higher level of expertise from deep learning developers, as they need to quantize models, and different boards support different model deployment frameworks with varying toolchain software.
Model Deployment Frameworks
The common mainstream model deployment frameworks mainly include: OpenVINO, TensorRT, ONNXRUNTIME,deep learning developers should at least master one deep learning model deployment framework. The best choice for model acceleration and inference on Intel CPU/GPU is OpenVINO; the best choice for model acceleration and inference on NVIDIA GPU is TensorRT; the best choice for high compatibility and support for model operators across different hardware platforms is ONNXRUNTIME. All three frameworks support C++ and Python languages and can run on multiple operating systems.Mastering these three mainstream deep learning model deployment frameworks can achieve optimal performance for model inference acceleration on different hardware platforms such as CPU, GPU, and AMD. OpenCV Academy has launched a systematic learning roadmap for deep learning deployment using OpenVINO, TensorRT, and ONNXRUNTIME. “To do a good job, one must first sharpen their tools”, for deep learning engineers, learning deployment before working is still timely; starting now is just right!
Deep Learning Model Deployment Roadmap Video Course
