Edge computing
Edge computing is a distributed computing approach that places computation and data processing near the users, devices, or sources and destinations of data.
LLMs in Production
A computing architecture that processes data near its source or point of use to reduce dependence on remote services.
- How it is used
- Positions edge inference as an option for LLM experiences where poor connectivity or API latency makes remote service unsuitable.
- How deep it goes
- Compares device constraints, accelerators, and model conversion paths, but stops short of a worked edge deployment.
- Examples
- Uses an offline immersive experience and an 8 GB Raspberry Pi with a Coral USB TPU to expose practical constraints.
- What you can do after
- Evaluate/understand when edge LLM inference merits exploration and which hardware and conversion constraints shape the choice.
Digital Twins in Action
Edge computing is a computing architecture that processes data near the device or data source rather than exclusively in a remote cloud.
- How it is used
- Places filtering, analytics, and decisions near physical assets so digital twins receive timely, relevant data without every raw sensor or video input.
- How deep it goes
- Explains placement, transmission choices, and a device-level case study, but does not specify a complete edge-to-cloud deployment procedure.
- Examples
- Uses selective video events, a YOLO webcam detector, and a water-meter camera that transmits extracted readings instead of images.
- What you can do after
- Evaluate/understand it by deciding what to process locally, what to transmit, and which field conditions require verification and tuning.
Server-Side WebAssembly
Edge computing performs computation near the sources or consumers of data rather than exclusively in centralized cloud infrastructure.
- How it is used
- Places computation near mobile devices, vehicles, sensors, and other data sources to reduce latency and bandwidth usage.
- How deep it goes
- Explains edge requirements through device heterogeneity, resource limits, hardware agnosticism, and deployment trade-offs.
- Examples
- Uses mobile devices, vehicles, sensors, self-driving cars, smart cities, and microcontrollers as deployment contexts.
- What you can do after
- Evaluate whether portable, low-memory execution suits applications distributed across constrained edge devices.
Latency
Edge computing is a computing approach that places computation and data processing near the users or devices that generate or consume the data.
- How it is used
- Reduces geographical and last-mile latency by colocating computation and data with users.
- How deep it goes
- Distinguishes near edge from far edge and explains latency rationale, but does not provide an implementation workflow.
- Examples
- Uses Tokyo–Virginia routing, nearby Cloudflare data centers, and an embedded SQLite REST API.
- What you can do after
- Evaluate near-edge and far-edge placements by their expected effects on geographic and last-mile latency.
Google Anthos in Action
Edge is a distributed computing environment located near the users, devices, or data sources that generate or consume workloads.
- How it is used
- Places latency-sensitive workloads near devices or users to reduce cloud dependence, data transfer, and response time.
- How deep it goes
- Defines deployment categories, fleet-management needs, and a smart-retail architecture, but stops short of installation procedures.
- Examples
- Uses autonomous driving, smart cities, factories, surveillance, augmented reality, remote surgery, and retail queue management.
- What you can do after
- Operate/configure distributed edge architectures that keep real-time processing local and delegate analytics or training to the cloud.
Related concepts
- edge deviceuses · An edge device is a computing device located near data sources, users, or operations that processes or uses data close to where it is generated or consumed.
- cloud computingcontrasts with · Cloud computing is the on-demand delivery of computing resources and services over a network from remotely managed infrastructure.
- computer visionused by · Computer vision is the field of computing concerned with using computational methods to extract and interpret information from images, video, and other visual data.
- deploymentkind of · Deployment is the process of making software, models, or other computational systems available to operate in a target environment.
- CDNincludes · A content delivery network is a distributed network of servers that caches or delivers digital content from locations near requesting users.
- meanaddresses · A mean is a measure of central tendency, calculated as the arithmetic average of values or, for a random variable, as the probability-weighted average of its possible values.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.