Services
AI/ML Infrastructure & MLOps Consulting for Production Workloads
CloudOps Velocity helps startups, SaaS companies, and AI-driven teams deploy, scale, secure, and monitor machine learning models, LLM applications, RAG systems, and GPU workloads on production-ready cloud infrastructure. We bridge the gap between model development and reliable production deployment.
Model Deployment
Deploy ML models, inference APIs, LLM apps, and intelligent automation workloads with secure cloud architecture.
GPU Infrastructure
Design GPU-enabled cloud environments for training, fine-tuning, inference, batch jobs, and high-performance AI workloads.
MLOps Pipelines
Automate model packaging, testing, CI/CD, versioning, environment promotion, monitoring, and rollback workflows.
Why AI/ML infrastructure needs a production mindset
AI and machine learning projects often fail after the prototype stage because the infrastructure is not ready for production. A model may work in a notebook, but production needs secure APIs, repeatable deployment workflows, scalable compute, model monitoring, observability, rollback, cost controls, and reliable access to data and embeddings.
Our AI/ML infrastructure consulting focuses on making AI systems usable in the real world: reliable inference, scalable model serving, safe deployment automation, secure access control, GPU cost awareness, and operations that your engineering team can maintain.
What we deliver
- AI/ML model deployment infrastructure on AWS, Azure, and Google Cloud
- GPU-based compute setup for training, fine-tuning, and inference workloads
- MLOps pipelines for model packaging, versioning, CI/CD, and rollout automation
- LLM application hosting, autoscaling, secure API deployment, and observability
- RAG architecture with embedding pipelines, vector databases, and retrieval workflows
- Model monitoring, logging, alerting, drift checks, and performance tracking
- Cost optimization for GPU workloads, AI APIs, storage, and inference patterns
- Security, access control, secrets management, compliance readiness, and auditability
AI platforms & tools we support
- AWS SageMaker, Bedrock, ECS, EKS, Lambda, API Gateway, S3, RDS, OpenSearch
- Azure Machine Learning, AKS, App Service, Functions, Blob Storage, Key Vault
- Google Vertex AI, GKE, Cloud Run, Cloud Storage, BigQuery, Pub/Sub
- Docker, Kubernetes, Helm, Terraform, GitHub Actions, GitLab CI, Jenkins
- Vector databases and search: Pinecone, Weaviate, Qdrant, Milvus, OpenSearch
- Monitoring and observability: Prometheus, Grafana, CloudWatch, OpenTelemetry
- LLM/RAG application infrastructure, APIs, queues, caching, and secure endpoints
Our AI/ML deployment process
1. Assessment
We understand your model, workload type, latency needs, data flow, security needs, and expected usage.
2. Architecture
We design the cloud infrastructure, deployment workflow, scaling model, monitoring, and cost controls.
3. Implementation
We build MLOps pipelines, APIs, containers, GPU environments, observability, and secure deployment automation.
4. Handover
We document the setup, train your team, and support ongoing AI infrastructure operations if needed.
Who this service is for
This service is built for teams that already have AI/ML models, prototypes, agents, or LLM applications and now need to make them production-ready.
AI/ML infrastructure outcomes we focus on
- Reliable AI/ML model deployments
- Scalable inference APIs and LLM applications
- Secure access control and secrets management
- Observable model performance and system health
- Cost-aware GPU and inference architecture
- Repeatable MLOps deployment workflows
- Production-ready RAG and vector database infrastructure
- Cleaner handover to engineering and data teams
Engagement models
AI Infrastructure Audit
A focused review of your model deployment approach, cloud setup, security, scaling, and cost risks.
MLOps Implementation
Best for teams that need model packaging, CI/CD, deployment automation, monitoring, and rollback workflows.
LLM/RAG Deployment
Best for teams building chatbots, agents, retrieval systems, embedding pipelines, and LLM-backed applications.
Planning an AI/ML deployment?
Share your AI workload, model type, expected traffic, and cloud goals. We’ll help you design production-ready infrastructure for deployment, scaling, monitoring, security, and cost control.
Frequently asked questions
Do you deploy AI and ML models to production?
Yes. We deploy ML models, LLM apps, RAG systems, and inference APIs on secure cloud infrastructure.
Can you help with GPU infrastructure?
Yes. We design GPU-enabled environments for training, fine-tuning, batch jobs, and inference workloads.
Do you build MLOps pipelines?
Yes. We build pipelines for model packaging, testing, versioning, CI/CD, monitoring, and rollback.
Can you help with LLM and RAG applications?
Yes. We help deploy LLM apps, chatbots, agents, vector databases, retrieval systems, and secure endpoints.
Can you optimize AI infrastructure costs?
Yes. We optimize GPU usage, autoscaling, inference patterns, model serving, storage, and vector databases.