Edge AI leverages the power of both Edge Computing and Artificial Intelligence to run machine learning tasks directly on connected edge devices. It provides a form of on-device AI to take advantage of rapid response times with low latency, high privacy, more robustness, and better efficient use of network bandwidth.
With a scope to improve performance, security and reduce latency in providing output, Edge AI is becoming a popular choice amongst AI developers, companies and manufacturers across different industry verticals.
Here are some articles we covered in our earlier articles that you might want to check out to learn more about Edge AI:
- What is Edge AI & what are the key challenges in Edge Ai today
- Drivers influencing the paradigm shift from Edge to Edge AI adoption
While adopting Edge AI is beneficial, infusion of AI with edge requires collecting data, training it, building a model, deploying it, and running various test modules. The entire process could take months and sometimes even years depending on the complexity of the models. Not to forget a load amount of effort and capital involved as well.
The essential role of Edge AI requires a vigorous structure, methodology and platform that produces the right output. Any failure in this could result negatively in the business outcome and product quality. Let’s say a self-driven car makes a mistake and causes casualties in an accident, it wouldn’t be a good scenario for the car manufacturer. The entire production part needs quicker response time while maintaining quality, accuracy, fairness and other elements to make a successful product.
This is where Edge AI Lifecycle Management comes into play. It helps AI developers in making their production pipeline complications simple, organized and efficient. Some of the phases or stages involved in Edge AI Lifecycle Management include:
- Deploying & Management
Stage 1: Train AI Models (This stage involves training models to detect objects, movement; teach them numbers, colours, etc)
AI is about algorithms that facilitates working of a function based on the intelligent decision making abilities. This is enabled by feeding large amounts of data, training the models through machine learning (ML) and deep learning to gather insights from data and automate tasks at scale. This step is very essential as the teaching of what has to be learnt is covered in this step.
A typical process of training AI Models can be something like this:
- Define use case of the AI Model to be trained
- Based on the use case, collect & prepare the dataset for AI Model
- Consider if deep learning neural networks are to be implemented for computer vision tasks
- Fix and decide on the parameters for getting optimal output and best possible outcome. For example, Batch Size, Epochs, etc.
- Fine tune parameters and retrain if the outcome is not satisfactory
Stage 2: Optimize AI Models (After the models are taught, they need to be optimised for achieving effective results)
Optimization plays an important role in Edge AI lifecycle as it makes or breaks the AI Model. Without proper optimization, the AI Models might not function properly or might not provide output as expected leading to misfunctioning of the hardware. The ability to act and react to certain inputs are decided at this stage.
It is very important to optimize the models in Edge AI as it helps in accelerating inference on edge and runs the model efficiently on hardware. Optimization also reduces the overall computational heaviness making it light-weighted to perform better and as expected.
Some of the techniques for optimizing models across different segments include:
- Hardware – GPU, TPU, ASIC, FPGA, Embedded.
- Software – Deep learning compilers, Target Optimized Libraries
- Algorithm – Pruning, Quantization
Using one of these layers, models can be optimized. By intuitively using several levels in conjunction, a significant speedup can be achieved.
You might be interested in- Post-Training Optimization Techniques
Stage 3: Deploying & Management (The last stage is infusing the trained and optimised model into hardware and making it ready for real-world action)
Deploying and managing AI Models is the last stage of Edge AI Lifecycle where the optimized and ready to infuse AI models are moved to the hardware and management post movement. This infusion of the model requires proper setup of the environment and execution of movement in the right way to achieve proper functioning of the system. If not properly managed, this stage can be cumbersome and might lead to improper functioning of the hardware.
Even after deployment, it is necessary to make sure to manage the model. Drifts in the model need to be managed as the new data collected requires retraining constantly till it is stable and expected outcome is achieved.
To successfully implement Edge AI, we need a cohesive and all-inclusive lifecycle management. While each stage has its own challenges and requirements, the ability to manage all these becomes tedious and lengthy. But properly managing these stages have great returns in the long term and helps in achieving both business and technology goals easier and faster.
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