Unlocking Smart Manufacturing Potential: Dell Technologies’ AI-Enhanced Edge Solution
Tue, 23 Apr 2024 09:16:08 -0000
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Manufacturers have always leveraged the latest technologies to enhance key outcomes such as productivity, efficiency, agility, and safety. However, the rapid acceleration of technology at the edge in recent years has left manufacturers grappling with the challenge of incorporating and integrating these innovations. This disparity creates a significant opportunity gap, favoring early adopters with a competitive edge.
A recent survey, Smart Factories Are Still a Work in Progress, highlights the struggle manufacturers face in integrating emerging technologies into their overall business strategy, as shown in the following figure. While AI is not new to manufacturers, only about one in five currently use it in their production operations.
Figure 1. Smart factory integration
Manufacturing customers encounter substantial challenges, particularly at the edge, when harnessing the potential of emerging technologies including AI. In addition to delivering critical functionality at the edge, the ideal solution must be:
- Easy to integrate, manage, and upgrade over its lifetime
- Designed to scale across diverse applications and systems
- Able to drive sustainability with greater digitization and efficiency of operations
- Able to securely protect the edge estate
Successfully meeting these demanding requirements in a sustainable manner necessitates innovative thinking and a robust ecosystem of partners to support the diverse needs of our manufacturing clients.
Say hello to the new and improved Dell Validated Design for Manufacturing Edge.
Dell Technologies’ extensive experience in the manufacturing and industrial sector, coupled with the trust of 82 percent of Fortune 100 companies using Dell for edge computing, has inspired us to enhance our edge solution with AI-driven features and to expand our partnerships.
We have accelerated key manufacturing outcomes by adopting AI and related technologies. This provided a comprehensive edge solution that bridges the gap between the promise of AI and its practical implementation on the factory floor with essential features such as:
- Simplified edge operations with Dell NativeEdge–Setting up AI capabilities on the plant floor is simplified. Dell NativeEdge streamlines deployment, ensures secure scaling without disrupting operations, orchestrates critical workloads, and manages the life cycle of the application and the endpoint automatically. These increased operational efficiencies drive greater sustainability, even in air-gapped environments.
- Strategic partnerships–Collaborating with Hyundai AutoEver (HAE), a Hyundai subsidiary, we deliver a crucial set of AI-driven capabilities and intelligent OT/IT convergence. HAE develops smart factory solutions such as NeoFactory IoT that integrate with existing IT/OT infrastructures, promoting streamlined operations and digital continuity across the manufacturing landscape.
- Flexibility of choice–Manufacturers can choose from a rich array of partners including Telit Cinterion, PTC, Litmus, Cognex, Claroty, and XMPro, each contributing unique capabilities. Whether it is edge analytics, connected intelligence, machine vision, predictive maintenance, or OT cybersecurity, the options are abundant.
- AI-driven insights–Dell Validated Design for Manufacturing Edge does not stop at deployment. It continues to evolve, adding features and supporting applications that enhance operational excellence. AI-driven insights empower decision-makers with real-time data, enabling proactive responses.
The Dell Validated Design for Manufacturing Edge empowers manufacturers to capitalize on their edge and leverage the potential of AI to gain business advantage. They do this by:
- Providing faster time to value by unifying IT and OT. This is done by eliminating data silos from edge devices from the factory floor to enterprise applications in the multicloud.
- Deploying innovative technologies for operational agility and improved production quality to expedite industry 4.0 and sustainability goals.
- Protecting the manufacturing edge from malicious actors using proven cybersecurity solutions.
Incorporating and integrating innovations quickly into production lines creates a competitive advantage. This end-to-end offering addresses this challenge from data collection and analysis to cybersecurity, all integrated and scalable in a single solution. The Dell Validated Design for Manufacturing Edge offers the best of both worlds, a trusted platform built by a proven market leader that is designed to accelerate manufacturing outcomes by leveraging AI.
To learn more about our validated designs for manufacturing edge, see Manufacturing Edge Solutions. Visit our exciting booth at Hannover Messe (#53, Hall 15).
Related Blog Posts
DevEdgeOps Defined
Mon, 25 Sep 2023 07:30:48 -0000
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What is a DevEdgeOps Platform?
As the demand for edge computing continues to grow, organizations are seeking comprehensive solutions that streamline the development and operational processes in edge environments. This has led to the emergence of DevEdgeOps platforms, with specialized processes and tools. Including frameworks designed to support the unique requirements of developing, deploying, and managing applications in edge computing architectures.
Edge Operations Shift Left
Shift Left refers to the practice of moving activities that were traditionally performed mostly at production stage to an earlier in the development stage. It is often applied in software development and DevOps to integrate testing, security, and other considerations earlier in the development lifecycle. Similarly, in the world of edge computing, we are moving operational tasks to an earlier stage, just like how we did with Shift Left in DevOps. We call this new idea, DevEdgeOps.
A DevEdgeOps platform facilitates collaboration between developers and operations teams, addressing challenges like network connectivity, security, scalability, and edge deployment management.
In this blog post, we introduce edge computing, its use cases, and architecture. We explore DevEdgeOps platforms, discussing their features and impact on edge computing development and operations.
Introduction to Edge Computing
Edge computing is a distributed computing paradigm that brings data processing and analysis closer to the source of data generation, rather than relying on centralized cloud or datacenter resources. It aims to address the limitations of traditional cloud computing, such as latency, bandwidth constraints, privacy concerns, and the need for real-time decision-making.
To learn more about the use cases, unique challenges, typical architecture and taxonomy refer to my previous post: Edge Computing in the Age of AI: An Overview
DevEdgeOps
DevEdgeOps is a term that combines elements of development (DevOps) and edge computing. It refers to the practices, methodologies, and tools used for managing and deploying applications in edge computing environments while leveraging the principles of DevOps. In other words, it aims to enable efficient development, deployment, and management of applications in edge computing environments, combining the agility and automation of DevOps with the unique requirements of edge deployments.
DevEdgeOps Platform
A DevEdgeOps platform provides developers and operations teams with a unified environment for managing the entire lifecycle of edge applications, from development and testing to deployment and monitoring. These platforms typically combine essential DevOps practices with features specific to edge computing, allowing organizations to build, deploy, and manage edge applications efficiently.
Key Features of DevEdgeOps Platforms
- Centralized edge application management—DevEdgeOps platforms provide centralized management capabilities for edge applications. They offer dashboards, interfaces, and APIs that allow operations teams to monitor the health, performance, and status of edge deployments in real-time. These platforms may also include features for configuration management, remote troubleshooting, and log analysis, enabling efficient management of distributed edge nodes.
- Integration with edge infrastructure—DevEdgeOps platforms often integrate with edge infrastructure components such as edge gateways, edge servers, or cloud-based edge computing services. This integration simplifies the deployment process by providing seamless connectivity between the development platform and the edge environment, facilitating the deployment and scaling of edge applications.
- Edge-aware development tools—DevEdgeOps platforms offer development tools tailored for edge computing. These tools assist developers in optimizing their applications for edge environments, providing features such as code editors, debuggers, simulators, and testing frameworks specifically designed for edge scenarios.
- CI/CD pipelines for edge deployments—DevEdgeOps platforms enable the automation of continuous integration and deployment processes for edge applications. They provide pre-configured pipelines and templates that consider the unique requirements of edge environments, including packaging applications for different edge devices, managing software updates, and orchestrating deployments to distributed edge nodes.
- Edge simulation and testing capabilities—DevEdgeOps platforms often include simulation and testing features that help developers validate the functionality and performance of edge applications in various scenarios. These features simulate edge-specific conditions such as low-bandwidth networks, intermittent connectivity, and edge device failures, allowing developers to identify and address potential issues proactively.
Final Words
The emergence of new edge use cases that combine cloud-native infrastructure and AI introduces an increased operational complexity and demands more advanced application lifecycle management. Traditional management approaches may no longer be sustainable or efficient in addressing these challenges.
In my previous post, How the Edge Breaks DevOps, I referred to the unique challenges that the edge introduces and the need for a generic platform that will abstract the complexity associated with those. In this blog, I introduced DevEdgeOps platforms that combine essential DevOps practices with features specific to edge computing. I also described the set of features that are expected to be part of this new platform category. By embracing these approaches, organizations can effectively manage operational complexity and fully harness the potential of edge computing and AI.
Edge Computing in the Age of AI: An Overview
Wed, 27 Sep 2023 05:19:01 -0000
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Introduction to Edge Computing
Edge computing is a distributed computing paradigm that brings data processing and analysis closer to the source of data generation, rather than relying on centralized cloud or datacenter resources. It aims to address the limitations of traditional cloud computing, such as latency, bandwidth constraints, privacy concerns, and the need for real-time decision-making.
Edge Computing Use Cases
Edge computing finds applications across various industries, including manufacturing, transportation, healthcare, retail, agriculture, and digital cities. It empowers real-time monitoring, control, and optimization of processes. This enables efficient data analysis and decision-making at the edge as it complements cloud computing by providing a distributed computing infrastructure.
Here are some common examples:
- Industrial internet of things (IIoT)—Edge computing enables real-time monitoring, control, and optimization of industrial processes. It can be used for predictive maintenance, quality control, energy management, and overall operational efficiency improvements.
- Digital cities—Edge computing supports the development of intelligent and connected urban environments. It can be utilized for traffic management, smart lighting, waste management, public safety, and environmental monitoring.
- Autonomous vehicles—Edge computing plays a vital role in autonomous vehicle technology. By processing sensor data locally, edge computing enables real-time decision-making, reducing reliance on cloud connectivity and ensuring quick response times for safe navigation.
- Healthcare—Edge computing helps in remote patient monitoring, telemedicine, and real-time health data analysis. It enables faster diagnosis, personalized treatment, and improved patient outcomes.
- Retail—Edge computing is used in retail for inventory management, personalized marketing, loss prevention, and in-store analytics. It enables real-time data processing for optimizing supply chains, improving customer experiences, and implementing dynamic pricing strategies.
- Energy management—Edge computing can be employed in smart grids to monitor energy consumption, optimize distribution, and detect anomalies. It enables efficient energy management, load balancing, and integration of renewable energy sources.
- Surveillance and security—Edge computing enhances video surveillance systems by enabling local video analysis, object recognition, and real-time threat detection. It reduces bandwidth requirements and enables faster response times for security incidents.
- Agriculture—Edge computing is utilized in precision farming for monitoring and optimizing crop conditions. It enables the analysis of sensor data related to soil moisture, weather conditions, and crop health, allowing farmers to make informed decisions regarding irrigation, fertilization, and pest control.
These are just a few examples, and the applications of edge computing continue to expand as technology advances. The key idea is to process data closer to its source, reducing latency, improving reliability, and enabling real-time decision-making for time-sensitive applications.
The Challenges with Edge Computing
Edge computing brings numerous benefits, but it also presents a set of challenges that organizations need to address. The following image highlights some common challenges associated with edge computing:
Edge Computing Architecture Overview
The following diagram represents a typical edge computing architecture and its associated taxonomy.
A typical edge computing architecture consists of several components working together to enable data processing and analysis at the edge. Here are the key elements you would find in such an architecture:
- Edge devices—These are the devices deployed at the network edge, such as sensors, IoT devices, gateways, or edge servers. They collect and generate data from various sources and act as the first point of data processing.
- Edge gateway—An edge gateway is a device that acts as an intermediary between edge devices and the rest of the architecture. It aggregates and filters data from multiple devices, performs initial pre-processing, and ensures secure communication with other components.
- Edge computing infrastructure—This includes edge servers or edge nodes deployed at the edge locations. These servers have computational power, storage, and networking capabilities. They are responsible for running edge applications and processing data locally.
- Edge software stack—The edge software stack consists of various software components installed on edge devices and servers. It typically includes operating systems, containerization technologies (such as Docker or Kubernetes), and edge computing frameworks for deploying and managing edge applications.
- Edge analytics and AI—Edge analytics involves running data analysis and machine learning algorithms at the edge. This enables real-time insights and decision-making without relying on a centralized cloud infrastructure. Edge AI refers to the deployment of artificial intelligence algorithms and models at the edge for local inference and decision-making. The next section: Edge Inferencing describes the main use case in this regard.
- Connectivity—Edge computing architectures rely on connectivity technologies to transfer data between edge devices, edge servers, and other components. This can include wired and wireless networks, such as Ethernet, Wi-Fi, cellular networks, or even specialized protocols for IoT devices.
- Cloud or centralized infrastructure—While edge computing emphasizes local processing, there is often a connection to a centralized cloud or data center for certain tasks. This connection allows for remote management, data storage, more resource-intensive processing, or long-term analytics. Those resources are often broken down into two tiers – near and far edge:
- Far edge: Far edge refers to computing infrastructure and resources that are located close to the edge devices or sensors generating the data. It involves placing computational power and storage capabilities in proximity to where the data is produced. Far edge computing enables real-time or low-latency processing of data, reducing the need for transmitting all the data to a centralized cloud or datacenter.
- Near edge: Near edge, sometimes referred to as the "cloud edge" or "remote edge" describes computing infrastructure and resources that are positioned farther away from the edge devices. In the near edge model, data is typically collected and pre-processed at the edge, and then transmitted to a more centralized location, such as a cloud or datacenter for further analysis, storage, or long-term processing.
- Management and orchestration—To effectively manage the edge computing infrastructure, there is a need for centralized management and orchestration tools. These tools handle tasks like provisioning, monitoring, configuration management, software updates, and security management for the edge devices and servers.
It is important to note that while the components and the configurations of edge solution may differ, the overall objective remains the same: to process and analyze data at the edge to achieve real-time insights, reduced latency, improved efficiency, and better overall performance.
Edge Inferencing
Data growth driven by data intensive applications and ubiquitous sensors to enable real time insight is growing three times faster than access network. This drives data processing at the edge to keep up with the pace and reduce cloud cost and latency. IDC estimates that by 2027 62% of enterprises data will be processed at the edge!
Inference at the edge is a technique that enables data-gathering from devices to provide actionable intelligence using AI techniques rather than relying solely on cloud-based servers or data centers. It involves installing an edge server with an integrated AI accelerator (or a dedicated AI gateway device) close to the source of data, which results in much faster response time.1 This technique improves performance by reducing the time from input data to inference insight, and reduces the dependency on network connectivity, ultimately improving the business bottom line.2 Inference at the edge also improves security as the large dataset do not have to be transferred to the cloud.3
Final Notes
Edge computing in the age of AI marks a significant paradigm shift in how data is processed, and insights are generated. By bringing AI to the edge, we can unlock real-time decision-making, improve efficiency, and enable innovations across various industries. While challenges exist, advancements in hardware, software, and security are paving the way for a future where intelligent edge devices are an integral part of our interconnected world.
It is expected that Inferencing market alone will overtake training with highest growth at the edge – necessitating competition in data center, near edge, and far edge.
For more information on how edge-inferencing works, refer to the next post on this regard: Inferencing at the Edge
1 https://steatite-embedded.co.uk/what-is-ai-inference-at-the-edge/
2 https://www.storagereview.com/review/edge-inferencing-is-getting-serious-thanks-to-new-hardware