Our agency will set up your ml infrastructure, mlops pipeline, and GPU deployment

Your ML model is only as good as the infrastructure running it. I build production MLOps pipelines that take your models from Jupyter notebooks to scalable, monitored, auto-scaling deployments.

What I Deliver:

ML model deployment (REST API, gRPC, batch inference), GPU/CPU infrastructure setup (AWS SageMaker, GCP Vertex AI, self-hosted), model serving (TensorFlow Serving, TorchServe, Triton, vLLM, Ollama), MLOps pipeline (MLflow, Kubeflow, DVC), training pipeline automation, model versioning and experiment tracking, A/B testing and canary deployments for models, auto-scaling inference endpoints, cost optimization for GPU workloads, and LLM deployment (self-hosted Llama, Mistral, fine-tuned models).

Why My Agency:

We sit at the intersection of DevOps and AI a rare combination. Most ML engineers can train models but struggle with production deployment. Most DevOps engineers can deploy apps but don't understand ML-specific challenges like GPU scheduling, model versioning, and inference optimization. We bridge both worlds.