AI, AIOps & MLOps | Predictive DevOps Intelligence | Stonetusker

Your Monitoring Tells You What Broke.
Ours Tells You Before It Does.

Most DevOps teams are reactive. An alert fires, an engineer looks into it, the incident is resolved. AIOps and MLOps change the pattern — detecting anomalies before they become outages, and automating ML model delivery with the same CI/CD rigour your engineering team already applies to application code.

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No retainers · NDA before any technical discussion · 30-minute call, no pitch deck

Two different problems,
both solved in the same engagement.

AIOps

Intelligence applied to DevOps operations

AIOps uses machine learning on your observability data — logs, metrics, traces — to detect anomalies before they cause incidents, reduce alert noise, correlate failure signals across services, and in some cases trigger automated remediation. The output is fewer incidents and faster resolution when incidents do occur, not just better dashboards to look at.

MLOps

DevOps rigour applied to ML model delivery

MLOps takes the CI/CD and IaC practices that work well for application code and applies them to machine learning models — versioned training, automated validation, reproducible environments, and one-click deployment from experiment to production. Models stop living in notebooks and start being delivered like software. Data drift and performance degradation are caught before they affect users.

What changes after an AIOps or MLOps engagement

50%+ Reduction in model release time — from the medical technology engagement

60% Fewer production incidents through AIOps predictive anomaly detection

1-click Model deployment from experiment to production — was a multi-day manual process

Full Audit trail on every model version — required for FDA-regulated AI in medical products

AIOps and MLOps capabilities, together or separately

AIOps

01 Anomaly Detection and Predictive Alerting ML models trained on your observability data to detect unusual patterns in metrics, logs, and traces before they cascade into incidents. Alerts fire on signals that matter, not on every threshold breach.

02 Alert Noise Reduction Correlation of related alerts across services so an on-call engineer gets one high-quality signal, not thirty alerts about the same root cause. Reduces alert fatigue and gets the right person to the right problem faster.

03 Automated Root Cause Analysis Correlation of logs, metrics, and traces across services to identify the probable root cause of an incident automatically. Engineers arrive at the incident with context, not a blank screen and a search query.

04 Intelligent Infrastructure Scaling Demand forecasting models that predict usage patterns and trigger scaling events before traffic peaks hit — not in response to them. Eliminates the gap between load spike and capacity increase that causes degraded performance during predictable events like scheduled batch jobs, marketing campaigns, or end-of-day reporting.

MLOps

01 Model CI/CD Pipeline Automated training, validation, and deployment pipelines for ML models using GitHub Actions, Jenkins, or a cloud-native alternative. Every model goes through the same reproducible process from experiment to production — no manual steps, no version confusion.

02 Model Versioning and Registry Every model version tracked, every experiment reproducible, every promotion from staging to production logged and auditable. Rollback to a previous model version is a command, not a manual exercise.

03 Drift Detection and Automated Retraining Monitoring for data drift and model performance degradation in production. When a model’s accuracy drops below defined thresholds, automated retraining workflows trigger with fresh data — without engineering intervention.

04 Compliance, Governance, and Audit Trails Full model lifecycle documentation for regulated environments — medical, financial, or industrial. Every training run, every dataset version, every deployment decision logged and traceable. Compliance evidence generated automatically through the pipeline, not assembled manually before an audit. Designed for teams working under FDA, ISO 13485, or financial services AI governance frameworks.

MLOps Pipeline for a US Medical Technology Company Building AI-Driven Cancer Detection

A US-based medical technology company developing AI-driven cancer detection products was releasing ML models slowly and inconsistently. Each release required manual steps across environments with no repeatable process, no version control on models, and no audit trail — a significant problem in a regulated medical device context where FDA requirements apply to AI model lifecycle management. We built a GitHub Actions-based CI/CD pipeline that automates the full model release process: training validation, environment consistency checks, version registration, and one-click deployment to production. Model release time dropped by over 50%. Every deployment now produces a complete, auditable record of the model version, training data, and validation results.

50%+ Reduction in model release time — from multi-day manual process to single pipeline run

1-click Deployment from validated experiment to production

Full Regulatory audit trail on every model version and deployment decision

Zero Manual steps in the model promotion process — was 12+ manual actions per release

What the client said

Before this engagement, releasing a model meant two days of coordination, manual checks, and hoping nothing had drifted between environments. Now it’s a single pipeline run with a complete audit trail we can hand to a regulator. Stonetusker understood both the ML side and the compliance requirements — that combination is rare.

VP of Engineering US Medical Technology Company

Read all published case studies

How we build intelligence into an existing DevOps setup

We identify your data sources and define what “normal” looks like

AIOps requires enough observability data to train on. MLOps requires an understanding of your current model training and deployment process. The engagement starts by mapping what data you have, what’s missing, and where anomalies would be most valuable to catch early. We sign an NDA before this starts. Your model architectures, training data, and operational patterns stay confidential.

We design predictive models or MLOps pipelines around your actual environment

AIOps anomaly detection models are trained on your specific metrics — not generic thresholds. MLOps pipelines are designed around your actual model types, frameworks, and deployment targets. Nothing is a template applied without adaptation. Your engineers review the design before we build it.

We integrate into your existing CI/CD and observability stack

AIOps layers over your existing monitoring. MLOps extends your existing CI/CD pipeline. Neither requires a wholesale replacement of what’s already working. Your engineers stay involved throughout so they understand the new systems and can maintain them independently.

We validate under real conditions before handing over

Anomaly detection models are calibrated against real traffic patterns. MLOps pipelines are tested with real model releases. Alert thresholds are tuned to minimise false positives while catching real signals. We don’t hand over an AI integration that’s only been tested against synthetic data.

Models improve over time — and we set up the loops to make that happen

AIOps models improve as they see more of your incident patterns. MLOps pipelines trigger retraining as production data evolves. Continuous learning is designed in from the start, not added later. Runbooks for model updates, retraining triggers, and drift responses are delivered before we step back.

AIOps & MLOps Pilot

One use case. Your data. Working results in 2 to 3 weeks.

A paid pilot that delivers a working AIOps anomaly detection model or a functioning MLOps deployment pipeline for one model — on your actual stack, not a sandbox. You see the result before committing to the full engagement.

Data and environment assessment We review your observability data quality, model pipeline, and current deployment process. We scope the pilot to the highest-value use case before any implementation starts.

Working AIOps model or MLOps pipeline Either a trained anomaly detection model running against your real observability data, or an automated ML deployment pipeline for one of your models — delivered within the pilot window, integrated into your actual environment.

Documentation and operating guide Model configuration, retraining triggers, calibration decisions, and how to extend the system to additional use cases — documented during the pilot so your team can operate it from day one.

Full scope for the remaining use cases At the end of the pilot: a specific proposal covering the additional AIOps and MLOps capabilities identified in the assessment, ordered by expected impact and implementation effort.

Pilot guarantee

If the pilot doesn’t deliver a working result on your actual data, you don’t pay for the full engagement.

The pilot produces a real, operating model or pipeline — on your actual observability data or your actual model stack, not on synthetic data or a demo environment. If it doesn’t, you don’t pay for the next phase. That’s in the agreement before the pilot starts.

Questions about AI in DevOps

We don’t have a data science team. Can we still benefit from AIOps or MLOps?

Yes — and most of the teams we work with don’t have dedicated data scientists either. AIOps doesn’t require your engineers to become ML practitioners. We build, train, and calibrate the models. Your team operates the resulting system through the same monitoring and alerting interfaces they already use, with better signals coming out of them. For MLOps, the same applies — if you have engineers who ship models (even if they call themselves data engineers or software engineers), we build the pipeline infrastructure around their existing workflow.

We already have Prometheus and Grafana with well-tuned alerts. What does AIOps add that we don’t already have?

Well-tuned static alerts are good — but they only fire when something crosses a threshold you’ve already anticipated. AIOps detects patterns that don’t match your normal baseline, even when they haven’t crossed a threshold. A memory leak that’s growing slowly. A latency pattern that’s slightly unusual at 3am on a Tuesday. A combination of metrics that individually look fine but together predict a failure in the next six hours. The main practical benefit for teams with good alerting is noise reduction — correlating related alerts so on-call engineers get one actionable signal instead of thirty redundant ones during an incident.

We work in a regulated industry. How does MLOps handle the compliance and audit requirements around AI model deployment?

This is the core of the medical technology case study and something we’ve designed for specifically. MLOps pipelines can generate the audit trail that regulators require — every training run logged with its dataset version, every model validated against defined acceptance criteria before promotion, every deployment recorded with a timestamp and the identity of what triggered it. Compliance evidence is produced automatically through the pipeline, not assembled manually before an audit. For FDA-regulated AI, ISO 13485, or financial services AI governance, we scope the compliance requirements into the pipeline architecture from the start. It’s significantly harder to add after the fact.

Your next incident has already started producing signals.

30 minutes. We arrive having looked at your current observability and deployment setup and we’ll tell you exactly where AIOps or MLOps would have the most impact first — and what the pilot would look like.

No retainers · No lock-in · NDA signed before we discuss your architecture or model pipeline