Model Deployment Optimizer

Optimizes ML model deployment for production inference including quantization (INT8/INT4), knowledge distillation, ONNX/TensorRT conversion, model pruning, batching strategies, and edge deployment for reducing latency and cost.

Platinum

v1.0.00 activationsAI & Machine LearningTechnologyexpert

SupaScore

85.4

Research Quality (15%)

8.5

Prompt Engineering (25%)

8.6

Practical Utility (15%)

8.8

Completeness (10%)

8.4

User Satisfaction (20%)

8.4

Decision Usefulness (15%)

8.5

Best for

▸Converting trained PyTorch/TensorFlow models to INT8/INT4 quantization for production inference
▸Optimizing LLM deployment with knowledge distillation and ONNX/TensorRT conversion for cloud GPUs
▸Implementing model pruning and batching strategies to reduce inference latency below 100ms SLAs
▸Deploying compressed models to edge devices with memory constraints under 1GB
▸Setting up vLLM serving infrastructure with optimal throughput and cost per token

What you'll get

●Step-by-step quantization pipeline with calibration datasets, performance benchmarks showing 3x speedup with <1% accuracy loss
●Complete TensorRT conversion script with optimization flags, memory usage comparison, and inference latency measurements
●Knowledge distillation training setup reducing model size by 4x while maintaining 95% of original performance

Not designed for ↓

×Training new models from scratch or fine-tuning existing models
×Data preprocessing pipelines or feature engineering for training
×MLOps orchestration platforms or experiment tracking systems
×Model accuracy improvement or hyperparameter optimization

Expects

A trained model with current inference performance metrics, target hardware specifications, and acceptable accuracy degradation budget.

Returns

Specific optimization recommendations with quantization strategies, conversion scripts, performance benchmarks, and deployment configuration files.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

model-deploymentinference-optimizationquantizationknowledge-distillationonnxtensorrtmodel-pruningedge-deploymentint8batchingvllmmlops

Research Foundation: 8 sources (5 paper, 3 official docs)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/15/2026

Initial release

Prerequisites

Use these skills first for best results.

ML Model Evaluation ExpertGold

Works well with

LLM Fine-Tuning StrategistGold ML Model Evaluation ExpertGold MLOps Platform EngineerGold PyTorch Deep Learning EngineerPlatinum TensorFlow/Keras EngineerGold

Need more depth?

Specialist skills that go deeper in areas this skill touches.

TF Serving Deployment ExpertGold Kubernetes Operations AdvisorGold Cloud Cost OptimizerGold

Common Workflows

Production Model Pipeline

End-to-end pipeline from model validation through optimization to production deployment monitoring

ML Model Evaluation Expert→model-deployment-optimizer→MLOps Platform Engineer

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