Turning Network Telemetry into Network Intelligence
Summary
By applying data engineering and machine learning to raw network telemetry, it’s possible to surface insights that would otherwise go unnoticed. Learn how this approach helps teams detect anomalies in real time, forecast capacity needs, and automate responses across complex, multi-domain environments.
There’s no shortage of data in modern NetOps. Streaming gNMI counters flow from routers every second, VPC Flow Logs pour out of AWS by the gig, and synthetic monitoring tools test SaaS reachability from all our branches. That doesn’t even include data like tickets, network diagrams, device config files, and more.
True network intelligence is still scarce – the holistic, contextual, and actionable understanding of what is going on in your network and why that matters. In other words, it is the ability to detect problems before users notice, predict capacity issues in advance, and troubleshoot incidents without an all-night Zoom call with every engineer who’s ever heard of TCP. Humans just don’t scale the way network data does.
Over the last few years, though, artificial intelligence has moved from vendor slideware to the actual building blocks that do exactly that. Network intelligence bridges the world of data and relevant insight more than ever before.
From data swamp to feature stream
AI can’t easily learn from a swamp of uncorrelated, raw CSV files. The first step in turning telemetry into intelligence is building a real‑time, normalized feature stream that merges device telemetry, cloud metrics, logs, and metadata into a standard timeline. In machine learning, a feature is basically a characteristic of the data that we can measure and is relevant to the result we’re looking for. Turning raw data into relevant features is the critical first step for network intelligence.
This includes ingesting timestamped telemetry from network devices like routers, load balancers, and firewalls, as well as from eBGP flow logs, gNMI, and whatever other data we use to enrich our telemetry database. And remember that much of this data is near real-time, so instead of relying solely on batch processing, we need to use stream processing mechanisms like Apache Flink or Spark to process data in flight and even perform ML feature engineering as it progresses through the pipeline.
However it’s built, we need a message backbone that partitions traffic properly and in-flight so that, for example, an interface counter and its corresponding cloud NAT translation reach the same consumer in order. The fun parts (read: ML models) become viable only after our telemetry pipeline is in place.
Detecting the invisible
Traditional alert rules fire when a single metric crosses a threshold, such as a single latency metric or link utilization metric. But today’s incidents often cross the boundaries of network devices, virtual network constructs, network adjacent services, and so on. This means the issues engineers deal with today are multi‑variate and beyond the scope of traditional static rules. If you think about it, many of the problems engineers deal with today aren’t readily observable in the raw data.
In the world of ML, there are various unsupervised anomaly detection methods that can ingest multi-dimensional vectors (input data) to surface what would otherwise be invisible. For example, by combining network metrics, logs, metadata, and so on, and then by learning the baseline for these metrics, a model can flag combinations in the data that have never happened at the same time. The invisible part is that no single metric is necessarily a problem; therefore, no alert would fire. However, a combination of these seemingly innocuous metrics could indicate a problem.
Here’s a hypothetical scenario: DBSCAN (or its streaming equivalent) is a commonly used algorithm which, in our example, can continuously cluster high‑dimensional features (which remember, are the characteristics of the data we care about) built from DNS query entropy, firewall CPU utilization, and per‑VPC egress packets per second.
Branch offices fall into tight clusters during normal operations that reflect regular business hours, learned SaaS patterns, and local break schedules (like lunchtime). When a single branch suddenly generates higher-entropy DNS queries and a 5% increase in firewall CPU without any rise in legitimate egress traffic, its feature vector lands far outside the established cluster.
The algorithm will flag this as noise, which we can interpret as an anomaly. So even though no individual metric exceeds a hard threshold, we’ve identified an issue, in this scenario, probably some type of DNS malware probing outbound connectivity.
Predicting potential problems
Prediction is where AI-powered network intelligence really starts to shine. Even the smartest operators with perfect data (and let’s face it, we never had perfect data) in the past would have struggled to predict incidents and be proactive rather than reactive. We’ve been doing this for years in other industries, such as healthcare, finance, ecommerce, retail, and others. This same predictive analysis also applies to networking, where network traffic is rarely random. Business‑hour peaks, nightly backups, month‑end financial jobs, and annual holiday traffic all repeat with relatively stable patterns. Accurately predicting utilization or loss requires a model that can analyze historical data to learn those patterns and project them forward.
For example, if we feed a year of five‑minute interface-utilization counters into a temporal convolutional network, one set of filters can learn the Monday‑morning ramp, another the quiet weekend shape. During inference, the network combines both to predict the next 48 hours of interface utilization.
In practice, this is more complex than running some copied code from Stack Overflow and requires careful planning around window size, normalization, model drift, etc. However, done correctly, a mature data pipeline and architecture allow a network intelligence platform to surface potential problems before they occur and even feed them back into orchestrators to change the network programmatically.
Responding to issues across domains
The final stage of network intelligence is automated action. This is where network intelligence merges traditional MLOps with the latest large language models and policy engines.
However, responding to incidents and troubleshooting programmatically can be very difficult because so much domain knowledge is embedded in the minds of human engineers. This is especially a problem in cross-domain environments.
Network intelligence combines an LLM’s power for semantic understanding, an MLOps workflow to surface issues, and automation workflows to generate the appropriate syntax, push config, and validate changes.
For example, programmatic change generation could include a fine‑tuned LLM model that turns high‑level intent (“add 1 Gbps headroom on any link predicted greater than 80% in the next 30 minutes”) into vendor‑specific configs or Terraform plans. It validates syntax, rolls out via GitOps, and attaches justification back to the incident ticket for traceability.
We’re relying on adding context through fine-tuning and semantic similarity, making our MLOps workflow more intelligent than traditional predictive analytics and functional across domains.
Think of a typical network monitoring dashboard. What looks like plain-old packet loss could actually be AWS NAT gateway saturation, and a poorly tuned HTTP client library could show up as BGP path changes when users give up and refresh. Context is what gives us understanding.
The road ahead
As 400Gb Ethernet, multi‑cloud overlays, and edge compute scatter dependencies across continents, traditional rule‑based monitoring just doesn’t scale. Network intelligence, rooted in mature data engineering, informed by ML, and monitored with deep observability, offers a path forward. It compresses the timeline from symptom to fix from hours to seconds, freeing engineers to focus on architecture instead of putting out fires.
