Operationalize AI with Control, Speed, and Reliability
AI innovation means nothing without operational excellence. Onyx helps organizations build, automate, and scale AI pipelines that are secure, auditable, and production-ready — enabling teams to move from experimentation to impact with confidence.
We design AI Operations (MLOps) systems that combine open-source flexibility with enterprise-grade reliability, integrating seamlessly across your infrastructure, whether sovereign, hybrid, or hosted on major cloud platforms.
Why It Matters
Most AI initiatives fail to move beyond the prototype stage. The reasons are always the same: fragmented tools, inconsistent environments, and lack of governance.
MLOps solves this by bringing together development, deployment, and monitoring into one controlled, repeatable system — turning AI into a real operational capability.
At Onyx, we make this operationalization transparent, secure, and sustainable, while keeping full flexibility on where your workloads run.
Our Principles
Open and Flexible
Built on interoperable frameworks and standards that avoid vendor lock-in.
Pragmatic Sovereignty
Support existing cloud ecosystems while guiding toward autonomous, sovereign operations.
Automation First
Every process — from data prep to model deployment — is automated for consistency and scalability.
Governed and Transparent
Full lifecycle traceability, ensuring compliance with internal and external standards.
Our Methodology
- 1. Assessment & Architecture. Evaluate current ML workflows and identify scalability or governance gaps.
- 2. Platform Design. Define a unified MLOps stack (cloud, hybrid, or on-prem) suited to your context and security posture.
- 3. Pipeline Automation. Implement reproducible, automated workflows for data ingestion, model training, and deployment.
- 4. Monitoring & Governance. Track model performance, detect drift, and ensure compliance through continuous observability.
- 5. Optimization & Scaling. Leverage orchestration tools (Kubernetes, Kubeflow, MLflow, etc.) to optimize performance and cost.
What We Deliver
End-to-End MLOps Platform Setup
Deployment of production-grade AI pipelines integrating model registry, CI/CD, and monitoring.
Hybrid & Multi-Cloud AI Operations
Manage distributed AI workloads across private nodes and hyperscalers with consistent policies and observability.
Model Lifecycle Governance Framework
Ensure full transparency, documentation, and compliance for every deployed model.
Continuous Training & Deployment Pipelines
Automate retraining and redeployment with real-time data updates and drift detection.
AI Reliability Engineering
Implement monitoring dashboards and alerting systems to ensure uptime, reproducibility, and trust.
Cloud Migration & Optimization
Transition existing AI workloads to sovereign or hybrid environments while maintaining compatibility with AWS, Azure, or Google Cloud.