AI, AIOps & MLOps - Predictive DevOps Intelligence
Stonetusker helps engineering and data teams embed intelligence into DevOps. From predictive incident detection to automated ML model delivery, we build pipelines that learn, adapt, and scale with your business.
Explore AI-Powered DevOpsSmarter Pipelines. Faster Feedback. Reliable Systems.
Modern engineering isn’t just about automation — it’s about anticipation. By combining DevOps best practices with AIOps and MLOps, Stonetusker enables teams to predict failures, optimize infrastructure, and automate model delivery with confidence.
Our Capabilities
- AIOps: Use machine learning on observability data to detect anomalies, reduce alert noise, and automate remediation.
- MLOps: Automate model training, validation, and deployment across environments. Implement CI/CD for AI models.
- Predictive DevOps: Anticipate build or deployment failures with intelligent scoring models and data-driven insights.
- Intelligent Infrastructure: Apply AI to scale resources dynamically based on predicted usage patterns.
- Automated Root Cause Analysis: Correlate logs, metrics, and traces to identify root causes faster.
- Continuous Learning Loops: Enable pipelines to self-improve as new data arrives — for both applications and ML models.
MLOps: Operationalizing Machine Learning
We build robust MLOps pipelines that unify data science, DevOps, and cloud infrastructure — ensuring every model is reproducible, testable, and deployable.
- Feature Store Integration: Manage and version key datasets for consistent model training.
- Model Versioning & Registry: Track and promote models through staging to production safely.
- Automated Retraining: Continuously update models with new data using triggers and schedules.
- Monitoring & Drift Detection: Detect performance degradation and automatically roll back or retrain.
- Compliance & Governance: Ensure full traceability and audit trails for ML assets.
Case Study: MLOps for the Medical product development
For a Medical US based medical technology company specializing in AI-driven cancer scanning partnered with Stonetusker to overcome bottlenecks in their ML model deployment process. Their model training and release cycles were slow, manual, and prone to inconsistencies between environments. We used the GitHub Actions based CI/CD to their Dev and Prod deployment. Deployment happens with click of a button
View More Success StoriesBusiness Benefits
- Up to 60% fewer incidents via predictive monitoring
- Faster ML model releases with automated retraining & deployment
- Improved reliability and uptime with AIOps automation
- Faster innovation through continuous feedback & learning loops
How We Build Intelligence into DevOps
- Data Discovery – Identify and collect observability, performance, and ML data sources.
- AI Model Design – Build anomaly detection and prediction models tuned to your environment.
- MLOps Integration – Automate training, testing, and deployment using modern CI/CD tooling.
- AIOps Automation – Integrate models into monitoring and alerting pipelines for real-time response.
- Continuous Optimization – Monitor drift, retrain models, and refine rules over time.
Why Choose Stonetusker
- Hands-on experience building intelligent CI/CD and data pipelines
- Deep understanding of both DevOps and machine learning ecosystems
- Tool-agnostic approach — we adapt to your stack (Kubernetes, AWS, Azure, GCP, etc.)
- End-to-end delivery: data prep → modeling → automation → operations
Make Your Pipelines Smarter
AI isn’t just for products — it’s for engineering, too. Let’s build intelligence into your DevOps, operations, and ML lifecycle today.
Discuss Your AIOps & MLOps Strategy