## The Problem With Sending Everything to the Cloud
When the "cloud first" era kicked in, the mental model for IoT was simple: sensors generate data, data goes to the cloud, cloud does the analytics. For many use cases, this works fine. But as IoT deployments have scaled into millions of devices generating terabytes of data daily, the cracks have appeared.
Bandwidth costs for sending all raw data to the cloud are substantial. Latency for decisions that need to happen in milliseconds (detecting a manufacturing defect, stopping machinery about to fail, adjusting a real-time process) is unacceptable if the decision requires a round-trip to a cloud. And connectivity — manufacturing floors, remote infrastructure, vehicles — often isn't as reliable as cloud architecture assumes.
Edge analytics addresses this by moving computation closer to the data. Rather than sending everything to the cloud, you run analytics at the edge — on local servers, on gateway devices, even on the IoT devices themselves — and only send meaningful results to the cloud. Raw data stays local; insights travel.
## What Belongs at the Edge vs. the Cloud
The decision about what to process at the edge versus the cloud is about latency requirements, data volume, and connectivity constraints.
At the edge: anomaly detection on sensor streams (process fault detection, predictive maintenance alerts), real-time quality control (defect detection in manufacturing), safety-critical decisions (emergency stop conditions), data filtering and reduction (sending only events and aggregates rather than raw data), and local inference from pre-trained ML models.
In the cloud: historical trend analysis, complex multi-source correlation, model training and retraining (on historical edge data), cross-site comparison and benchmarking, long-term storage and compliance archiving.
The key insight is that the cloud is excellent at handling large volumes of data for non-time-critical analysis, while the edge is necessary for time-critical decisions and practical where bandwidth is constrained. Design your analytics architecture to use both appropriately rather than defaulting entirely to either.
## Edge AI: Running ML Models at the Edge
The ability to run trained ML models at the edge — rather than just rule-based logic — is transforming what's possible. Computer vision models running on edge devices detect manufacturing defects, security breaches, and safety events without needing to send video streams to the cloud. Anomaly detection models running on industrial IoT gateways identify equipment issues before they become failures.
The key enabling technologies are model compression (making large ML models small enough to run on constrained edge hardware), hardware acceleration (specialised chips from NVIDIA, Intel, and others that run ML inference efficiently on edge devices), and ML lifecycle management (tools for deploying, updating, and monitoring models across large fleets of edge devices).
NVIDIA's Jetson platform and Google's Coral Edge TPU are the leading hardware options for ML inference at the edge. Both provide orders-of-magnitude better performance than CPU-based inference for typical computer vision and anomaly detection models.
## Managing Edge Infrastructure at Scale
The operational challenge of edge analytics is fleet management. When you have hundreds or thousands of edge devices across multiple sites, managing software deployments, monitoring health, and ensuring security across all of them is a significant challenge.
Kubernetes for edge (K3s, MicroK8s) is increasingly the standard for managing containerised applications at the edge. It provides the same application deployment model as cloud Kubernetes, making it much easier for development teams to build applications that run consistently at both edge and cloud.
OTA (over-the-air) updates are critical. Edge devices must be updateable remotely — both for feature updates and for security patches. A fleet of IoT devices that can't be patched remotely is a security liability as well as an operational nightmare.
*Lara IT Solutions designs and deploys edge analytics solutions for industrial and enterprise IoT. Contact us on 0330 043 1930.*