Machine Learning Infrastructure Consulting That Ships to Production
Your model works in the notebook. Now it needs to work in production — at scale, with governance, without burning through your GPU budget.
Our US-based engineers build and operate the machine learning infrastructure — GPU-enabled Kubernetes platforms — that take AI from experiment to production.
Talk to an engineerWhy Choose THNKBIG for AI Infrastructure and MLOps
THNKBIG is a US-based machine learning infrastructure consulting firm with offices in Texas and California, specializing in GPU-optimized Kubernetes platforms for machine learning workloads. We bridge the gap between data science and production operations, helping organizations move from notebook experiments to scalable inference systems.
Our MLOps consulting expertise spans the full ML lifecycle: data pipelines, distributed training infrastructure, model serving with KServe and Triton, and the governance frameworks enterprises require. We understand GPU economics deeply, implementing scheduling policies and spot instance strategies that routinely deliver 30-50% cost reductions.
Organizations partner with THNKBIG to accelerate their AI initiatives without burning through GPU budgets. We build platforms that let your data scientists focus on models while your operations team gains the observability and control needed to run ML workloads in production confidently.
The model isn't the hard part
Your data scientists built something remarkable in a Jupyter notebook. Now someone has to make it run in production — reliably, at scale, within budget, with governance. That's where most AI initiatives stall.
87%
of ML models never reach production
60%
of ML team time spent on infrastructure
40%
average GPU utilization (industry)
Production ML infrastructure, not science projects
We build the complete MLOps platform — from data pipeline to production inference — on Kubernetes infrastructure designed for GPU workloads.
Data Pipeline
High-throughput data ingestion and preprocessing at scale. We architect pipelines that handle petabytes without breaking your budget.
Training Infrastructure
GPU-optimized Kubernetes clusters designed for ML training workloads. Multi-node distributed training, checkpointing, and experiment tracking.
Model Serving
Production inference at scale with KServe, Triton, or custom serving solutions. Auto-scaling, A/B testing, and canary deployments included.
Operations & Governance
The part everyone forgets: monitoring, alerting, cost controls, and compliance. Your AI platform needs operational discipline, not just notebooks.
Stop wasting money on idle GPUs
GPU infrastructure is expensive. Most organizations run at 40% utilization or less. We fix that.
Problem
40% average GPU utilization
Solution
Right-sized scheduling and bin-packing
Outcome
94% utilization achieved
Problem
$1.2M/month GPU spend
Solution
Spot instances + preemption handling
Outcome
$340K/month savings
Problem
3-day training jobs failing
Solution
Checkpointing + distributed training
Outcome
4-hour training cycles
Problem
Weeks to deploy a model
Solution
GitOps + KServe automation
Outcome
Same-day deployment
A reference MLOps platform we've shipped to production
From data pipeline to GPU-scheduled inference — every layer built for cost control and reliability.
Data & Feature Pipeline
Feature stores · data versioning · distributed processing
GPU-Scheduled Training
NVIDIA GPU Operator · MIG partitioning · multi-node distributed training
Model Registry & GitOps
MLflow · Kubeflow · ArgoCD-driven promotion
Model Serving
KServe · Triton Inference Server · auto-scaling inference
Production Applications
A/B testing · canary rollout · model monitoring
Observability
DCGM Exporter · Prometheus · Grafana
GPU utilization, drift detection, and inference latency tracked in real time.
Cost Governance
Bin-packing · spot instances · idle detection
GPU spend tracked against actual utilization, not provisioned capacity.
Series C AI company cuts GPU costs by $340K/month
Technology
The Challenge
A Series C AI company was spending $1.2M/month on GPU infrastructure with only 40% average utilization. Training jobs took 3 days and frequently failed. Model deployment required manual intervention and took weeks.
Our Approach
- Audited GPU scheduling and identified utilization bottlenecks
- Implemented GPU bin-packing and fractional GPU allocation
- Deployed spot instance strategy with graceful preemption handling
- Built GitOps pipeline for model deployment with KServe
- Implemented distributed training with automatic checkpointing
Results
$340K/mo
GPU cost savings
94%
GPU utilization
3x
Faster training
Same-day
Model deployment
Frequently asked questions
We already have data scientists. Why do we need infrastructure help?
Your data scientists are experts at building models — not at running Kubernetes, optimizing GPU scheduling, or building production serving infrastructure. We handle the platform so they can focus on the science. Most ML teams we work with spend 60%+ of their time on infrastructure. We flip that ratio.
What's the difference between MLOps and regular DevOps?
MLOps adds model-specific concerns: experiment tracking, model versioning, feature stores, training infrastructure, serving infrastructure, model monitoring, and drift detection. A DevOps team that hasn't worked with ML workloads will miss critical requirements around GPU scheduling, data pipelines, and model lifecycle management.
Can you work with our existing ML tools?
Yes. We work with MLflow, Kubeflow, Ray, Weights & Biases, SageMaker, Vertex AI, and most major ML platforms. We're not here to replace your tools — we're here to make them work together in production.
How do you handle GPU costs?
GPU costs are the #1 concern for every AI team we work with. We implement: right-sized GPU allocation (most teams over-provision), bin-packing to maximize utilization, spot/preemptible instances with graceful handling, time-boxing for training jobs, and idle detection with automatic scale-down. Typical savings: 30-50% of GPU spend.
What about on-prem GPU clusters?
We've deployed Kubernetes on on-prem NVIDIA DGX clusters, custom GPU servers, and hybrid environments. On-prem adds complexity around GPU drivers, networking, and storage — but it's often the right choice for cost or data sovereignty reasons. We'll help you decide and implement either path.
How long until we see results?
GPU cost optimization typically shows results in 2-4 weeks — it's often quick wins around scheduling and right-sizing. Full MLOps platform buildout is typically 8-12 weeks to production-ready state. We deliver incrementally, so you're seeing value throughout the engagement.
Do you train models?
No. We build the infrastructure that trains models. We're platform engineers, not data scientists. We'll make your ML team dramatically more productive, but we won't build your models for you.
Technology Partners
Related Reading
The AI Infrastructure Gap: Why Demos Don't Deploy
93% of orgs deploy AI models less than daily. Here's what's missing in the infrastructure layer.
Running GPU Workloads on Kubernetes
A practical guide to GPU scheduling, node configuration, and AI/ML workload orchestration.
THNKBIG Partners with Run:ai for GPU Orchestration
How Run:ai's GPU virtualization platform helps enterprises maximize AI infrastructure ROI.
AI Infrastructure and MLOps Consulting for Production Workloads
Running AI workloads and machine learning infrastructure at enterprise scale requires more than cloud compute — it requires orchestration expertise, GPU scheduling knowledge, and MLOps practices that keep models moving from training to production efficiently. THNKBIG's machine learning infrastructure consulting practice helps organizations design, build, and operate the Kubernetes-based platforms that power their AI and ML workloads.
Our machine learning infrastructure consulting covers GPU cluster design on Kubernetes, NVIDIA GPU operator deployment, model serving infrastructure with tools like vLLM and Triton Inference Server, and MLOps pipeline implementation using Kubeflow, MLflow, and Argo Workflows. We work with organizations building AI infrastructure on AWS, Azure, and GCP — as well as on-premises GPU clusters for organizations with data sovereignty or latency requirements. Our team has deployed large-scale AI workloads for enterprises in healthcare AI, financial services analytics, and autonomous systems development.
AI infrastructure is only as effective as the MLOps practices surrounding it. THNKBIG implements end-to-end machine learning infrastructure that connects data pipelines, experiment tracking, model registry, and deployment automation. We help organizations reduce model deployment time from weeks to hours, implement automated retraining pipelines, and establish the monitoring infrastructure needed to detect model drift in production AI workloads.
Ready to make AI operational?
Whether you're planning GPU infrastructure, stabilizing Kubernetes, or moving AI workloads into production — we'll assess where you are and what it takes to get there.
US-based team · All US citizens · Continental United States only