# AI Infrastructure & MLOps

AI Infrastructure & MLOps

AI /  AI Infrastructure

Building an AI demo is easy. Running models at scale, with GPUs humming and endpoints up, is another story. Whether you're fine-tuning a custom model, running open-source LLMs, or just tired of burning cash on inefficient pipelines — we’ll help you build the infrastructure that lasts. We bring real MLOps, GPU orchestration, model lifecycle management, and cost-optimized deployment pipelines to your AI stack.

Scalable, secure, GPU-accelerated infrastructure for AI apps that actually run in production.

## What We Build

- **MLOps Toolchains**: CI/CD pipelines for training, testing, deploying, and monitoring models.

- **GPU Orchestration**: On-demand and auto-scaling GPU clusters (NVIDIA, AMD, cloud-native or bare metal).

- **Model Serving Infrastructure**: Real-time inference endpoints with load balancing, batching, and A/B testing.

- **LLM Hosting Platforms**: Ollama, LM Studio, HuggingFace Accelerated Inference, vLLM, TGI — all supported.

- **Vector Databases & Embeddings**: Pinecone, Weaviate, Qdrant, FAISS, or in-house setups.

- **Observability & Cost Controls**: GPU usage tracking, autoscaling rules, monitoring, alerting, and logging.

- **Open-Source Model Optimization**: Quantization, pruning, distillation, model packaging and rollout.

- **Custom AI DevOps Environments**: Notebook infra, remote training clusters, secure sandboxed runtimes.

### Use Cases We Support

- Internal LLM deployments — on-prem, in VPC, or cross-cloud.
- Scalable RAG apps with managed vector search and cost-efficient inference.
- Training and fine-tuning jobs on multi-GPU or TPU environments.
- Dev environments for research teams with notebooks and experiment tracking.
- AI-enabled SaaS platforms that need infrastructure built to scale.

### Supported Clouds, Platforms, & Frameworks

- AWS SageMaker, Bedrock, ECS, EKS
- GCP Vertex AI, GKE, TPUs
- Azure ML
- Kubernetes, Docker, Terraform
- LangChain, PyTorch, TensorFlow, Ray, JAX
- Weights & Biases, MLflow, Comet, DVC

### Why Conflict™?

- We’ve built AI infra that runs inside highly regulated industries — and on a single founder’s MacBook.
- We balance velocity, security, cost, and performance — no hand-wavy abstractions.
- We don’t treat MLOps as an afterthought. It’s built into our process.

### Contact Us

Make It Real — And Make It Scale

Don’t let infra be your bottleneck. We’ll help you ship AI that doesn’t just work — it works under load.