Introduction
Machine vision training demands copious amounts of image data to be captured, processed and analyzed to develop an AI model. With the wide adoption of digital transformation in manufacturing industries, we are witnessing the integration of powerful technologies such as machine vision with Machine Learning (especially Deep Learning). However, the applications of such amazing integrations are accompanied by an ever-increasing data flow. Broadly, two kinds of computing models can help industries train their vision solutions: Edge computing and Cloud computing. To find out the apt model for our application, we must first understand each type of computing model.
What is Edge Training?
Edge training or on-premise training is the training that occurs at or near the physical location of the user or the source of data. In simple words, machine vision-related computing takes place in physical, computational resources situated on the premise itself. Edge training falls within the broader category of edge computing.
One common use case of edge computing is the modern-day self-driving car. Since these machines demand prompt real-time responses, self-driving cars need a local processing solution, as in edge computing. Edge computing is also used across several industries such as manufacturing, telecommunications, utilities, to name a few.
What is Cloud Training?
With the concept of cloud computing industry 4.0 gaining prominence, cloud training has emerged as a newer kind of training. As the data flow increases in size, on-premise expansion in terms of storage and processing power would bring in more operational and maintenance costs. Over the past few years, with the rise of cloud computing industry 4.0, companies have started turning to off-site storage and data analytics at the lowest costs and fastest speeds.
In simple words, cloud computing is like renting computational resources according to your needs from cloud service providers. You neither have to buy expensive computational resources nor employ a tech team to handle its maintenance and performance. If you ever need more processing power or storage, you can just rent more resources from cloud services.
Some prominent cloud computing services that have come up with the rise in cloud computing industry 4.0 and are driving digital transformation in manufacturing industries are the following:
- AWS
- Microsoft Azure
- Google Cloud Platform
Benefits of the Cloud & Edge Hybrid System
The hybrid computational model offers a secure, consistent, and faster experience with the data processing happening close to the data sources, and the storage and management take place in a centralized repository in the cloud. This model also provides more flexibility in the movement of data depending on some key factors such as security, time, etc. for example, the data for time-critical decisions can be stored close, and the historical, industrial data can be saved in the cloud.