Machine Learning
Teaching systems to predict failure from the monitoring data they already produce, instead of only reporting it after the fact.
What this covers
Machine learning is an area TecFlax is building toward, and the reason we think it fits us is specific rather than fashionable: we already run the systems that generate the data.
Every monitored estate produces years of metrics, logs and incident records. Almost all of it is used reactively, consulted only once something has already broken. The same history is exactly what a model needs in order to recognise the shape of a failure before it completes: the slow memory leak, the disk filling on a predictable curve, the transaction success rate drifting down over a fortnight.
That is the work we are aiming at first. Predictive maintenance on infrastructure, capacity forecasting so growth is planned rather than discovered, and reducing alert noise by learning which alerts historically mattered and which were ignored. Each of those builds directly on monitoring engagements we already deliver.
Machine learning needs good data, and most estates do not have it yet because nobody was collecting with this in mind. Getting the monitoring and logging right is therefore not a prerequisite we are inventing to sell more work. It is genuinely the first step, and it is valuable on its own even if no model is ever trained.
Where we are today
- Predictive maintenance from infrastructure and application metrics
- Capacity forecasting so growth is planned, not discovered
- Alert noise reduction learned from what your team actually acted on
- Failure pattern recognition across historical incidents
- Data collection and retention designed for future modelling
- Realistic expectations about what your current data can support
At a glance
- StatusIn development
- Builds onMonitoring and logs
- PrerequisiteGood historical data
- First stepData readiness review