Optimized Edge Computing for Machine Learning: Common Requirements and Real-World Use Cases
Edge computing enables faster processing, reduced latency, and greater control over data privacy and security by bringing computing processes closer to the source of data generation. As a result, the adoption of edge computing is expected to increase by 28% through the end of the decade. This growth is driven by the improved user experience that consumers get from intelligent applications that are powered by machine learning (ML) deployed directly onto their devices at the edge. However, the management of ML models at the edge can be challenging due to the complex nature of edge environments. These edge environments involve a large number of endpoints with varying computing resources, limited bandwidth, and intermittent connectivity, making it difficult to maintain the reliability of these models.
In this article, we will explore the common requirements of optimized edge computing across industries for ML and the challenges of managing ML models at the edge. We will also discuss the importance of Model Performance Management (MPM) and how to implement it for edge ML deployments.
Different Real-World Edge Use Cases for different industries
Edge ML is being utilized in a wide range of industries to increase the output and safety of heavy equipment, decrease resource usage in production, and automate critical tasks:
- Consumer Electronics: Home assistants like Amazon Echo and Google Home use natural language processing and ML algorithms to respond to voice commands without sending data to the cloud for processing. This approach allows for faster response times and better user experiences.
- Industrial Automation: Manufacturing and industrial sectors use ML at the edge to optimize processes and increase efficiency. Edge computing devices can process data from sensors and machines in real-time, predicting failures and triggering maintenance before a breakdown occurs.
- Healthcare: In Healthcare, ML is being utilized in the area of cardiology to detect anomalies in heart activity that may indicate an impending cardiac event. Wearable devices provide real-time monitoring of heart activity, and algorithms running on edge devices can analyze the data generated to detect potential cardiac events.
- Retail: Retailers are deploying models directly onto edge devices to personalize marketing campaigns, optimize inventory management, and improve the in-store experience for customers. By running the models on edge devices, retailers can make decisions and take actions in real-time without transferring data to a cloud environment for analysis.
- Agriculture: ML at the edge is playing a vital role in improving crop production and reducing waste in agriculture. Farmers can leverage the power of edge devices and sensors to monitor soil moisture, nutrient levels, and other environmental factors in real-time, enabling them to make data-driven decisions about irrigation, fertilizer application, and harvesting.
Common Requirements of Real-World Edge Use Cases
ML has a variety of edge use cases, but they all share some standard requirements for edge computing applications.
- Ability to handle real-time/streaming data: In many edge use cases, the ML model must perform inference in real-time. For instance, in smart cities, traffic cameras need to analyze and detect traffic patterns to make real-time traffic predictions. Similarly, in retail, cameras must quickly recognize products and shoppers to provide personalized recommendations.
- Ability to take a model trained in the cloud and deploy it in a different environment: While some ML models can be trained on the edge, most require extensive computing power and resources, making training difficult at the edge. Therefore, it is often more practical to train models in the cloud or on-premises, and then deploy them to the edge.
- Ability to manage the full model lifecycle (deployment, observability, testing, and optimization) to many edge endpoints from a central location: The endpoint for a model at the edge might not be a single location but rather a fleet of devices. How do you manage the feedback loop from the edge devices in the field back for continuous analysis and improvement?
All these standard requirements can pose significant challenges without an efficient and effective edge computing solution for ML deployment. For instance, most deployment solutions on the market aren’t designed to accommodate inferencing in real-time while providing monitoring to ensure the model remains accurate over time. By understanding these common requirements and edge computing example, you can more easily identify some of the newer solutions that seek to address these challenges and provide optimized edge use case for machine learning.
Predictive Maintenance At The Edge
Predictive maintenance is one of the many edge computing applications that can be implemented to reduce equipment downtime, improve maintenance processes, save costs, and minimize the risk of catastrophic failure. In this edge computing example, we explore how industries are using predictive maintenance to monitor equipment and vehicles in real-time and detect issues before they become major problems.
The P-F curve is a reference model that helps illustrate the relationship between equipment performance and potential failure. In this edge use case, the P-F curve shows that as time passes, the equipment’s performance degrades until it reaches a certain point where a potential failure can occur.
In order to successfully implement predictive maintenance on the edge, it’s crucial to have a complete architecture that includes data collection, storage, pre-processing, modeling, and analysis support. Additionally, tools for deployment, management, and monitoring are important considerations as well. Let’s explore these stages in more detail.
- Data Collection:
The first task when implementing predictive maintenance using ML is data collection. This involves collecting data from various sensors on the equipment such as vibration, temperature, and pressure sensors. The data collected is then stored in a data lake for further processing.
- Data Preprocessing:
Next is data preprocessing, where the raw data is cleaned, transformed, and prepared for analysis. This involves activities such as removing missing values, filtering out noise, and converting data to a suitable format for machine learning.
- Feature Engineering:
Now relevant features will be extracted from the preprocessed data. Feature engineering involves identifying patterns in the data that can be used to train the machine learning model. This stage is critical because the accuracy of the machine learning model depends on the quality of the features.
- Machine Learning Model Training:
Once the features are identified, the next step is to train the machine learning model using the preprocessed data. Several machine learning algorithms can be used to train the model, including Random Forest, Gradient Boosted Trees, and Deep Learning models. The goal is to develop a model that can accurately predict equipment failure.
- Model Deployment:
The trained machine learning model is then deployed to the edge devices, where it can be used to monitor the equipment in real-time. The model is deployed using containerization technologies such as Docker and Kubernetes, which enable easy deployment and management of the model.
- Model Monitoring:
Lastly, model monitoring is critical in edge computing applications, where the model’s performance is continuously monitored to ensure it is providing accurate predictions. This involves tracking metrics such as precision, recall, and false-positive rates. If the model’s performance degrades over time, it may be necessary to retrain the model with new data or adjust the model’s parameters.
By ingesting data from edge devices, training models, deploying the models using containerization, and monitoring the deployed models, this type of architecture provides a scalable, fault-tolerant, and efficient way of performing predictive maintenance at the edge.
Optimizing Edge ML with Wallaroo
Wallaroo provides a solution to the challenges of deployment by offering a platform that can handle real-time data at the edge. Deploying ML models at the edge has become increasingly important as more and more devices generate data at the edge of the network. Deploying at the edge allows for faster decision-making and improved operational efficiency, but it also comes with its own set of challenges, such as limited computing resources, network latency, and the need to optimize models for specific hardware architectures.
Wallaroo’s unique deployment architecture is designed specifically for implementing machine learning models at the edge. With its ability to facilitate feedback loops between edge devices and cloud infrastructure, Wallaroo’s approach enables enterprises to achieve optimized edge computing and simplify model deployment while ensuring efficient and effective ML models. This means that enterprises can make the most of their ML investments and achieve improved operational efficiency, making it an ideal platform for deploying models at the edge.
If you’re interested in learning more about Wallaroo and how it can benefit your operations at the edge, request a demo today. With Wallaroo, you can confidently achieve success with your MLOps at the edge, without the headache.
