Cognitive AI Toolkit
MLOps as a Service
End-to-end lifecycle management: GitOps CI/CD, containerized serving with GPU autoscaling, RAG endpoints, safety guardrails, drift and cost monitoring.
Prod endpoints
84
Inferences / day
4.8M
p50 latency
180 ms
Uptime SLO
99.95%
Functional capabilities
Mapped to the RFP functional requirements
CI/CD Pipeline for ML
GitOps deployment, IaC, SBOM, image signing and promotion gates.
Containerized Model Serving
Multi-tenant serving of LLMs, vision, embeddings with OpenAI-compatible APIs, GPU autoscaling, guardrails, RAG endpoints, hybrid search and reranking.
Real-Time Performance Monitoring
Accuracy, latency, throughput and anomaly alerts.
Drift Detection
Data, concept and prediction drift with auto-retrain triggers.
Model Registry
Versioned registry with lineage and promotion state.
Automated Retraining
Scheduled and event-driven retrain pipelines.
Rollout Strategies
Canary, blue-green and A/B with automatic fallback.
Observability & Audit
OpenTelemetry, prompt/response logging, immutable audit trail.
Who uses this
- MLOps Engineer
- Platform SRE
- FinOps Analyst
Runs on approved hyperscalers
GCP
Google Cloud (Vertex AI)
me-central2 (Doha)
AZURE
Microsoft Azure (AI Foundry)
qatarcentral
OCI
Oracle Cloud (OCI AI Services)
me-jeddah-1 / qatar-doha-1