We are witnessing the increased use of IoT sensors, even in traditional sectors such as healthcare. Be it smart factories or cities, supply chains, connected homes, or cars, this sensor network deployed for these use cases takes immediate action based on incoming data.
However, first transmitting data to the cloud and then receiving execution instructions creates a time lag. This process of hauling intelligence to the cloud and getting inputs needs to become more efficient and must happen in single-digit milliseconds. To alleviate this challenge and reduce latency, real-time data computing has moved to the edge.
Edge computing captures, stores, processes, and analyses data close to the location where it is needed to improve response times, ensure low latency, and save bandwidth. This distributed computing framework brings applications closer to data sources such as sensors and IoT devices.
With edge computing, cloud services move from the network core to the network edges to drive agile service responses and optimize the traffic load of the network.
However, while edge computing does accelerate responses, the growing number of mobile and IoT devices generate massive volumes of multi-modal data that networks find hard to manage. This explosion of devices can lead to cloud congestion and can also open up security vulnerabilities.
Why Do Edge Devices Need Cloud-Based Machine Learning?
There's an increasing need to reduce the time it takes to go from data ingestion to action to meet the latency needs of process automation. Businesses must identify ways to manage, process, and leverage edge data more optimally. This demands determining ways to ensure that data packets do not take circuitous, value-reducing routes around the network.
The solution to this lies in moving the decision intelligence to the edge using machine learning (ML). Doing this allows enterprises to use the edge data appropriately to make real-time, intelligent decisions and drive a positive impact on their bottom line.
Integrating Cloud-Based Machine Learning with Edge Devices
Most machine learning models are processor-hungry and demand generous numbers of parallel operations. This leads to a dependency on cloud computing and needs machine learning to run in the central data centers. This, in turn, often compromises security, costs, and most importantly, latency.
Every interaction between enterprises and their customers today is a mix of multiple touchpoints and hybrid technologies that need fast access to the device, data, and applications. Ensuring such speed is imperative for creating impactful new experiences and power-positive end-user experiences.
However, transporting datasets to distant clouds via networks does not enable this. By using machine learning with edge computing, enterprises can gather insights, identify patterns, and initiate patterns faster.
Also Read: Machine Learning for Cloud Resource Allocation
How Does Edge Machine Learning Work?
Edge machine learning brings machine learning models locally to edge devices and can be invoked by edge applications. Machine learning at the edge becomes significantly important today, as mentioned above.
In many scenarios, raw data is collected from sources that are far from the cloud and might have specific restrictions or needs. These could include poor connectivity to the cloud, real-time prediction needs, legal restrictions, and regulatory demands. Such restrictions can prevent sending data to external services or large datasets that need to be pre-processed before responses are sent to the cloud.
Preventive maintenance, defect detection in production lines, driving safety and security functions, etc., are some of the use cases that can benefit from having machine learning at the edge.
An edge solution that uses machine learning consists of an edge application and a machine learning model that runs on this application. Edge machine learning controls the lifecycle of one or more ML models deployed to the edge devices.
The machine learning model can start at the cloud side and end at a standalone deployment of the model on the edge device. Different scenarios demand different ML model lifecycles, which can be composed of many stages that include data collection and preparation, model building, compilation, deployment on the edge device, and so on.
It is important to note that the machine learning at edge function does not apply to the application lifecycle. Decoupling the machine learning model lifecycle and application lifecycle provides you the independence and flexibility to keep evolving them at different paces when needed.
Strategizing Edge Machine Learning
An edge machine learning strategy coupled with cloud support permits today's organizations to deliver uniform application and operational experiences. It allows them to reach remote locations that find it hard to maintain continuous connectivity with the data center. To enable this, it is important to:
- Run consistent deployment models from the core to the edge.
- Ensure architectural flexibility to address connectivity and data management needs.
- Identify automation requirements needed to automate and manage infrastructure deployments and updates from core data centers to the edge site.
- Identify and address data security challenges of the edge environment.
- Build and operationalize ML models using DevOps and GitOps principles.
Wrapping Up
Edge machine learning addresses the latency challenge, distributes computing load, and delivers better real-time, real-world outcomes. Unlocking new performance levels and opportunities becomes easier with machine learning at the edge.
To ensure that this technology delivers, however, the role of an experienced technology partner in making the right infrastructure, architecture, technology, and design choices cannot be overstated. Connect with our experts to know more.
