Startup & AI
AI / ML Ops Consulting. $99/hr.
From notebook to production — ML pipelines, model deployment, MLOps platforms, inference optimization, and GPU infrastructure that turns experiments into reliable services.
What We Deliver
ML Pipelines
End-to-end training pipelines — data ingestion, feature engineering, model training, evaluation, and artifact storage with full reproducibility.
End-to-end training pipelines — data ingestion, feature engineering, model training, evaluation, and artifact storage with full reproducibility.
Model Deployment
Production serving infrastructure — REST/gRPC endpoints, batch prediction jobs, A/B testing, and canary rollouts for model updates.
Production serving infrastructure — REST/gRPC endpoints, batch prediction jobs, A/B testing, and canary rollouts for model updates.
MLOps Platform
Kubeflow, MLflow, or SageMaker-based platforms — experiment tracking, model registry, automated retraining, and collaboration across data science teams.
Kubeflow, MLflow, or SageMaker-based platforms — experiment tracking, model registry, automated retraining, and collaboration across data science teams.
Inference Optimization
Model quantization, batching strategies, GPU sharing, and Triton Inference Server tuning — reduce latency and cost per prediction by 50-80%.
Model quantization, batching strategies, GPU sharing, and Triton Inference Server tuning — reduce latency and cost per prediction by 50-80%.
Model Monitoring
Data drift detection, prediction quality tracking, feature importance monitoring, and automated alerts when model performance degrades.
Data drift detection, prediction quality tracking, feature importance monitoring, and automated alerts when model performance degrades.
GPU Infrastructure
GPU cluster management — spot instance strategies, multi-GPU training, CUDA optimization, and cost-effective scaling for training and inference workloads.
GPU cluster management — spot instance strategies, multi-GPU training, CUDA optimization, and cost-effective scaling for training and inference workloads.
Why Choose Platform-Projects
$99/hr
Standard Rate
48hrs
Time to Start
10+ yrs
Engineer Experience
0
Long-Term Contracts
Who This Is For
Data scientists build great models but getting them to production takes months — handoff to engineering is a bottleneck
Models in production but nobody knows if accuracy has degraded — no monitoring, no drift detection, no retraining triggers
Retraining is manual — someone runs a notebook, exports weights, and deploys by hand every time the model needs updating
GPU costs climbing with no visibility — training jobs running on expensive instances with no spot strategies or resource optimization
Technology Stack
Kubeflow · MLflow · SageMaker · Vertex AI · TensorFlow Serving · Triton · Ray · DVC · Weights & Biases · Seldon · BentoML · ONNX
Frequently Asked Questions
How much does ML Ops consulting cost?
Our standard rate is $99/hr for senior ML engineers. Urgent or after-hours work is $149/hr. A typical MLOps platform setup runs 120-200 hours — far less than hiring a dedicated ML platform team.
Which MLOps platform should we use?
SageMaker if you’re all-in on AWS, Vertex AI for GCP, and Kubeflow or MLflow for cloud-agnostic setups. We evaluate your team’s workflow, model complexity, and deployment targets to recommend the best fit.
Can you speed up our model inference?
Yes. Common wins include model quantization (INT8/FP16), dynamic batching, GPU sharing with Triton, and ONNX conversion. Most models see 2-5x latency improvement and 50-70% cost reduction per prediction.
Do you help with LLM deployment and fine-tuning?
Absolutely. We deploy and fine-tune LLMs using vLLM, TGI, or managed endpoints. We handle RAG pipelines, prompt engineering infrastructure, evaluation frameworks, and cost optimization for inference at scale.
$99/hr
Senior ML engineers, $99-$149/hr. No contracts.
Ready to Get Started?
AI / ML Ops Consulting — starting within 48 hours.
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