AI, AIOps & MLOps — Predictive DevOps Intelligence

Smarter Pipelines. Faster Feedback. Reliable Systems.

Modern DevOps goes beyond automation — it’s about prediction. By integrating AI, AIOps, and MLOps, Stonetusker enables teams to anticipate failures, improve uptime, and automate model delivery with real-time intelligence.

Our Capabilities

• AIOps: Apply machine learning to observability data for anomaly detection, alert noise reduction, and automated incident remediation.
• MLOps: Automate model training, validation, and deployment using modern CI/CD and cloud-native pipelines.
• Predictive DevOps: Anticipate build or deployment failures using predictive analytics and intelligent scoring models.
• Intelligent Infrastructure: Use AI to dynamically scale resources based on usage forecasts and performance metrics.
• Automated Root Cause Analysis: Correlate logs, metrics, and traces for faster incident resolution.
• Continuous Learning Loops: Enable your systems to self-learn and adapt with every release and dataset.

MLOps: Operationalizing Machine Learning

We build robust MLOps implementation and consulting pipelines that unify data science, DevOps, and infrastructure — ensuring every model is reproducible, testable, and securely deployable.

• Feature Store Integration: Manage and version critical datasets for consistent training and evaluation.
• Model Versioning & Registry: Safely promote models from development to production.
• Automated Retraining: Continuously update models with fresh data through scheduled retraining workflows.
• Monitoring & Drift Detection: Detect and mitigate performance drift automatically.
• Compliance & Governance: Ensure full audit trails and compliance for AI model lifecycle management.

Case Study: MLOps for Medical Product Development

A U.S.-based medical technology company specializing in AI-driven cancer detection partnered with Stonetusker to streamline their ML model deployment. Their training and release cycles were slow and inconsistent across environments. We implemented a GitHub Actions–based CI/CD pipeline that automates deployment with a single click — cutting model release time by over 50%.

Business Benefits

• Up to 60% fewer incidents through predictive analytics
• Faster ML model releases with automated retraining & delivery
• Improved reliability with AIOps-driven monitoring & response
• Accelerated innovation through self-learning, data-driven pipelines

How We Build Intelligence into DevOps

1. Data Discovery: Identify observability, performance, and ML data sources.
2. AI Model Design: Develop predictive and anomaly detection models tuned to your systems.
3. MLOps Integration: Automate model lifecycle — from training to deployment — via CI/CD.
4. AIOps Automation: Embed AI-driven decisioning in monitoring and alerting pipelines.
5. Continuous Optimization: Retrain and refine models as your system evolves.

Why Choose Stonetusker

• Hands-on experience building intelligent CI/CD and data pipelines
• Deep understanding of DevOps, AI, and ML ecosystems
• Tool-agnostic expertise across Kubernetes, AWS, Azure, and GCP
• End-to-end delivery — from data ingestion to predictive operations