What is MLOps & LLMOps? MLOps (Machine Learning Operations) and LLMOps (Large Language Model Operations) are specialized DevOps practices for deploying, monitoring, and maintaining AI models at enterprise scale. Our agentic platform delivers self-healing pipelines that autonomously optimize model deployment, reducing deployment time by 26x while achieving 99.9% uptime and 50% cost reduction.
Transform AI deployment with autonomous MLOps pipelines, real-time LLM monitoring, and self-healing infrastructure. Trusted by enterprises managing 10,000+ models in production.
Autonomous pipelines, predictive scaling, and self-healing infrastructure accelerate every deployment stage
LLMOps requires specialized approaches beyond traditional MLOps practices
Production-grade infrastructure supporting 10,000+ models across multi-cloud environments
Real-world applications across industries
Deploy 500+ fraud detection models daily across 12 regions
Automated MLOps pipeline with compliance checks and A/B testing
Deployment time reduced from 4 hours to 9 minutes (26x faster)
Monitor hallucination rates in patient-facing chatbots
Real-time LLMOps monitoring with semantic validation
99.7% accuracy maintained, 35-point NPS increase
Handle 10M daily predictions with <50ms latency
Auto-scaling infrastructure with predictive resource allocation
50% infrastructure cost reduction, 99.9% uptime
MLOps focuses on deploying traditional machine learning models (classification, regression) with structured inputs and numeric outputs. LLMOps is specialized for Large Language Models, handling unstructured text inputs, managing prompt templates, monitoring token usage, and detecting semantic issues like hallucinations. LLMOps requires context-aware evaluation metrics that traditional MLOps tools don't provide.
Agentic MLOps uses autonomous AI agents to eliminate manual steps in the deployment pipeline. These agents automatically select optimal deployment strategies, predict resource requirements, execute canary deployments, and roll back failures without human intervention. This automation reduces deployment time from hours to minutes—achieving 26x speed improvements compared to manual workflows.
Our platform provides real-time monitoring for model performance metrics, data drift detection, concept drift analysis, prediction latency tracking, error rate alerts, and resource utilization. For LLMs specifically, we add token usage optimization, hallucination detection, prompt latency monitoring, and semantic drift analysis to ensure production reliability.
Yes, our MLOps platform supports multi-cloud deployment across AWS, Azure, and Google Cloud Platform. You can manage models across all three clouds from a unified control plane, with automated failover, cross-cloud traffic routing, and consistent governance policies regardless of where models are deployed.
We build governance into the deployment pipeline with automated compliance checks, audit trail logging, role-based access control (RBAC), model versioning with lineage tracking, and approval workflows. All model changes are tracked, and you can enforce policies for data privacy, explainability requirements, and regulatory compliance (GDPR, HIPAA, SOC 2) before production deployment.
Start your free POC and discover how autonomous MLOps/LLMOps pipelines can transform your AI deployment process.