Hybrid Cloud/Edge Architecture for Operationalizing Machine Learning Models in OT

TL;DR;

Efficient operationalization of machine learning (ML) and deep learning (DL) algorithms in the industrial sector presents significant challenges due to the inherently probabilistic nature of these methods, the constant need for retraining, and their critical dependence on high frequency data sampling/inference.

To address these challenges, this article summarizes a proposed a hybrid cloud/edge architecture, integrating centralized computing resources (cloud) with decentralized, local resources (edge) as shonw in the original paper [1].

Motivation and Context

The integrated application of ML in Cyber-Physical Systems (CPS) is essential for smart manufacturing, delivering substantial gains in flexibility, agility, and operational robustness. However, effective implementation requires a systemic approach that enables both vertical and horizontal integration of data and models throughout the entire production chain—also known as Cyber-Physical Production Systems (CPPS). In this context, Continuous Delivery (CD) practices become essential to support the centralized and ongoing deployment of trained models across distributed industrial environments.

Key Challenges in Industrial Implementation

• Technological Heterogeneity: A wide range of equipment, non-standard communication protocols, and various data formats.

• Data Management: The need for high-quality, adequately volumed, and efficiently integrated structured and unstructured data.

• Sustainable Model Operation: Continuous monitoring, support for incremental learning, and guaranteed low latency due to the critical and time-sensitive nature of industrial processes.

• Non-Technical Challenges: Shortage of skilled labor, high stakeholder expectations, and concerns about cost-effectiveness of tech implementations.

Proposed Architecture

The proposed hybrid cloud/edge architecture is composed of five key domains:

1. Edge System

Handles time-sensitive tasks such as data acquisition, local pre-processing, real-time inference, and direct interaction with physical devices (sensors and PLCs).

2. Cloud Instance

Conducts more complex, less time-sensitive tasks such as system-wide monitoring, analytical dashboards, and advanced incremental learning techniques.

3. Integrated Data Management

Provides economical, long-term storage for large volumes of data, with local caches enabling near-instantaneous access for critical industrial processes.

4. Development and Experimentation

Covers the full cycle of ML/DL model creation, experimentation, and optimization, supported by rigorous version control and experiment tracking.

5. Machine Learning Operations (MLOps)

Involves continuous and automated processes for integration (CI), delivery (CD), and training (CT), which are essential to dynamically maintain and update ML models.

Proposed hybrid architecture for MLOPs/AIOps purposes in manufacturing.

Advantages of the Hybrid Architecture

• Significant Latency Reduction: Local inference ensures fast, effective responses in critical operations.

• Optimized Bandwidth Usage: Local pre-processing greatly reduces the volume of data transmitted to the cloud.

• Greater Flexibility and Scalability: Complex model training can be performed in the cloud while simplified versions run at the edge.

• Improved Security and Operational Reliability: Minimizes external risks and ensures continuity of operations even when cloud connectivity is lost.

Implementation Challenges

• High technical complexity in integrating cloud and edge layers.

• Substantial initial investment in advanced technological infrastructure.

• Complex security management in distributed environments.

• Ongoing need for maintenance and frequent updates on both layers.

Conclusion and Strategic Relevance

The hybrid cloud/edge architecture is essential for ensuring scalability and economic efficiency in deploying ML solutions in complex industrial environments.

By combining the analytical power of the cloud with the speed and operational efficiency of edge computing, this approach holds significant potential to optimize operational performance, improve product quality, and enhance the overall robustness of production processes.

This article contributes to both academic and practical understanding of the strategic importance of smart integration between cloud and edge computing, highlighting its critical role as a competitive differentiator in Industry 4.0.

Reference:

[1] A reference architecture for the operationalization of machine learning models in manufacturing