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
Amazon Web Services (AWS) is the subsidiary of Amazon that provides on-demand cloud computing platforms and APIs on a metered pay-as-you-go basis. As of 2017, AWS holds 33% of the cloud.
- Microsoft Azure
Azure is a cloud computing service by Microsoft for the building, testing, deploying, and managing applications through Microsoft-managed data centers.
- Google Cloud Platform
Google Cloud Platform (GCP), provided by Google, is a suite of cloud computing services on resources that Google uses internally for its products such as Gmail, YouTube, etc.
Difference Between Edge & Cloud Training
Businesses that are planning to invest in some IoT product of vision system development might confront the challenge of evaluating and picking the most suitable computation model for their application. Let us now compare edge training and cloud training on some key metrics:
- Scalability
Expanding on-premise processing resources can prove to be expensive and arduous. If there is a need, you can always upscale or reduce your processing and storage capabilities with cloud computing.
- Latency
Edge computing provides a faster, more consistent experience. Since the computing occurs locally, this enables faster responses to the data source or the end-users which facilitates faster decision making. Also, edge computing enables IoT devices to communicate even in offline or low bandwidth conditions, reduce network costs and transmission delays.
- Processing power & Storage
Due to obvious reasons, edge computing comes with limited processing resources and functionalities. Cloud computing can ease the intensity associated with model training and AI-based algorithms, making it suitable for applications that have intensive processing requirements like deep learning. Cloud computing also facilitates the backup of critical enterprise data.
- Cost-effectiveness
Cloud training helps businesses save a lot on capital expenses due to the flexibility of not owning and maintaining the on-site computing servers/databases and the use dependant paying model of the cloud service platforms.
- Data security
Data security remains one significant roadblock to cloud training. The majority of companies are hesitant to push production-level data into a centralized environment like the cloud. This is because of liability exposure and the probable repercussions if the data is accessed by unintended people.
What is a Cloud & Edge Hybrid System?
By now, we understand that both cloud and edge computing have their set of benefits and challenges. While edge computing has the edge over cloud computing in terms of response time and reliability in poor network conditions, the ever-evolving needs of businesses demand a centralized hub like the cloud. Thankfully, there is another alternative that combines the benefits of both types, called hybrid computing.
In terms of machine vision, more intensive processing tasks such as AI model development and training can be performed on the cloud. The actual inspection part or the inferencing, which is computationally less demanding, can be performed on the edge. The hybrid model also enables businesses to leverage the best of private and public cloud by integrating them. A modern-day example is today’s smart fitness watch.
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.
Conclusion
In this blog post, we discussed the fundamentals of two training/computational models, namely edge training and cloud training. We then understood the benefits and challenges related to both types of models. In the end, we explored another computational model – the hybrid model followed by why it is a better alternative to drive the digital transformation in manufacturing industries.
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