AI Infrastructure & ML Ops Consulting
Build scalable infrastructure for LLM deployment, GPU orchestration, and production ML workloads. From prototype to production at scale.
AI/ML Infrastructure Challenges
Moving from notebook to production requires specialized infrastructure expertise
💸 GPU Cost Explosion
A100/H100 GPUs cost £2.4-4/hour. Poor utilization wastes thousands monthly on idle resources.
⏱️ Slow Inference
LLM serving requires optimization. Unoptimized deployments = slow responses and poor UX.
🔧 ML Ops Complexity
Model versioning, A/B testing, monitoring, and retraining pipelines require specialized tooling.
Production-Ready AI Infrastructure
Deploy, scale, and optimize AI workloads with confidence
🎮 GPU Orchestration
Maximize GPU utilization with intelligent workload scheduling and resource allocation
- Kubernetes GPU scheduling
- NVIDIA MIG (Multi-Instance GPU)
- Auto-scaling based on demand
- Spot instance optimization
🚀 LLM Deployment
Deploy and serve large language models with optimized inference performance
- vLLM, TensorRT-LLM optimization
- Model quantization (4-bit, 8-bit)
- Ray Serve for distributed inference
- OpenAI-compatible API endpoints
📊 ML Ops Pipelines
Automate training, deployment, and monitoring workflows
- MLflow for experiment tracking
- Kubeflow Pipelines automation
- Model registry and versioning
- A/B testing infrastructure
🗄️ Vector Databases
Scale embedding storage and similarity search for RAG systems
- Pinecone, Weaviate, Qdrant
- pgvector for PostgreSQL
- Hybrid search (vector + keyword)
- Index optimization strategies
Technologies & Platforms
Model Serving
TensorFlow Serving, TorchServe, vLLM, Ray Serve, Triton Inference Server
Frameworks
PyTorch, TensorFlow, JAX, Hugging Face Transformers, LangChain
Orchestration
Kubernetes, Kubeflow, MLflow, Airflow, Prefect
Cloud Platforms
AWS SageMaker, GCP Vertex AI, Azure ML, Lambda Labs, RunPod
Common Use Cases
🤖 Custom LLM Deployment
Deploy open-source LLMs (Llama, Mistral, Mixtral) with optimized inference for your use case
Outcome: 10x cost savings vs. API pricing, data privacy, custom fine-tuning
📚 RAG System Infrastructure
Build retrieval-augmented generation systems with vector databases and embedding pipelines
Outcome: Accurate, hallucination-free responses grounded in your data
🔄 ML Training Pipelines
Automate model training, evaluation, and deployment with CI/CD for machine learning
Outcome: Faster iteration, reproducible experiments, automated retraining
Typical Results
GPU Cost Reduction
Through utilization optimization and spot instances
Faster Inference
With quantization and optimized serving
Deployment Time Saved
Automated ML Ops pipelines vs. manual processes
Ready to Scale Your AI Infrastructure?
Let's discuss your ML workloads and build infrastructure that scales
30-minute call to review your AI infrastructure needs
Frequently Asked Questions
Should I use managed services or build custom infrastructure?
Depends on scale and requirements. Managed services (SageMaker, Vertex AI) work well for getting started. Custom infrastructure gives more control and cost savings at scale. I'll help you choose the right approach.
How much can I save with GPU optimization?
Typical savings: 60-80% through spot instances, autoscaling, and utilization optimization. A single A100 GPU costs £2.94/hour on-demand—spot pricing and shared instances dramatically reduce costs.
Do you support specific ML frameworks?
Yes—PyTorch, TensorFlow, JAX, and transformer libraries (Hugging Face). Infrastructure is framework-agnostic, but I optimize serving and deployment for your specific stack.
What's included in ML Ops setup?
Experiment tracking (MLflow), model registry, automated training pipelines, A/B testing infrastructure, monitoring dashboards, and CI/CD for model deployment. Fully automated from training to production.