deploying-machine-learning-models

This skill enables Claude to deploy machine learning models to production environments. It automates the deployment workflow, implements best practices for serving models, optimizes performance, and handles potential errors. Use this skill when the user requests to deploy a model, serve a model via an API, or put a trained model into a production environment. The skill is triggered by requests containing terms like "deploy model," "productionize model," "serve model," or "model deployment."

1.02x

Quality

44%

Does it follow best practices?

Impact

92%

1.02x

Average score across 9 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./backups/skills-migration-20251108-070147/plugins/ai-ml/model-deployment-helper/skills/model-deployment-helper/SKILL.md

Evaluation results

95%

-1%

Fraud Detection Model API

REST API model serving

Criteria

Without context

With context

REST endpoint defined

100%

Model loaded at startup

100%

Input field validation

100%

Invalid input response

100%

Exception handling

100%

Error response on exception

100%

Prediction returned

100%

Requirements analysis comment

60%

50%

No hardcoded credentials

100%

Runnable server code

100%

Input schema documented

100%

94%

-1%

Productionizing a Sentiment Analysis Model

Docker and Kubernetes deployment

Criteria

Without context

With context

Dockerfile present

100%

Base image specified

100%

Dependencies installed in image

100%

Kubernetes Deployment manifest

100%

Resource limits set

100%

Replica/scaling config

100%

Kubernetes Service manifest

100%

YAML used for deployment config

100%

Monitoring settings present

100%

Requirements analysis note

100%

Config validation script

37%

25%

No hardcoded secrets

100%

94%

Production Model Observability and Recovery

Performance monitoring and rollback

Criteria

Without context

With context

Latency tracking

100%

Throughput tracking

100%

Anomaly alerting

100%

Monitoring runs continuously

100%

Rollback script present

100%

Rollback identifies previous version

100%

Rollback steps documented

100%

Monitoring metrics logged

100%

Requirements analysis present

87%

62%

Model accuracy tracked

37%

75%

No large file generation

100%

87%

97%

13%

Serving a Text Sentiment Model with Preprocessing

Preprocessing pipeline integration

Criteria

Without context

With context

Preprocessing module

100%

Text cleaning steps

100%

Preprocessing called before inference

100%

Input validation present

100%

Validation rejects bad input

100%

Exception handling around inference

62%

Meaningful error on exception

16%

100%

Requirements analysis documented

62%

100%

Assumptions about model format

100%

Serving entry point present

100%

requirements.txt provided

100%

Preprocessing is reusable

100%

No hardcoded credentials

100%

Deploying a Price Prediction Model to a Cloud Serving Platform

Cloud-based serving platform deployment

Criteria

Without context

With context

Requirements analysis first

100%

Cloud platform identified

100%

Deployment strategy justified

100%

Input validation present

100%

Validation error response

100%

Exception handling

100%

Error response on exception

100%

config.yaml present

100%

Monitoring setup

75%

100%

Latency or throughput addressed

100%

Real-time serving pattern

100%

No hardcoded secrets

100%

requirements.txt provided

100%

README deployment steps

100%

84%

-8%

Automating and Documenting a Model Deployment Workflow

Deployment workflow documentation and automation

Criteria

Without context

With context

Workflow steps in order

100%

80%

Analysis step present

100%

Config validation before deploy

100%

validate_config.py checks fields

100%

Step execution logged

100%

Error handling in workflow

100%

deployment_plan.md present

100%

Plan explains why

100%

API/serving code generated

75%

25%

Data validation in serving

Monitoring configured

100%

87%

requirements.txt provided

100%

75%

No hardcoded credentials

100%

Rollback referenced

100%

92%

-5%

Overnight Churn Prediction Pipeline

Batch inference service deployment

Criteria

Without context

With context

Requirements analysis present

100%

75%

Batch processing loop

100%

Per-record input validation

100%

Invalid records handled gracefully

100%

Exception handling around inference

100%

Error records reported

100%

Throughput metric tracked

100%

Monitoring output present

100%

Deployment automated

62%

25%

Output file produced

100%

Model loaded once

100%

No hardcoded credentials

100%

84%

Integrating a Sentiment Model into the Customer Feedback Platform

Integration with preprocessing and monitoring pipeline

Criteria

Without context

With context

Requirements analysis first

87%

100%

Preprocessing step included

100%

Preprocessing is a separate function

100%

Input validation present

100%

Validation rejects bad input

100%

Exception handling around inference

25%

Monitoring integration present

100%

Latency recorded

100%

Throughput or request count tracked

25%

50%

Deployment automated

62%

50%

API endpoint defined

100%

No hardcoded credentials

100%

92%

Production Launch: Price Optimization Model

Automated deployment with performance monitoring

Criteria

Without context

With context

Requirements analysis document

100%

Analysis precedes implementation

100%

Serving code generated

100%

Input validation present

100%

Validation error response

100%

Exception handling in serving

Deployment step present

100%

Latency monitoring

100%

Throughput monitoring

100%

Monitoring output persisted

100%

End-to-end automation script

100%

No hardcoded credentials

100%

requirements.txt present

100%

Repository: jeremylongshore/claude-code-plugins-plus-skills
Commit: 13d35b8

Evaluated: about 2 months ago
Agent: Claude Code
Model: Claude Sonnet 4.6

Table of Contents

Fraud Detection Model API Productionizing a Sentiment Analysis Model Production Model Observability and Recovery Serving a Text Sentiment Model with Preprocessing Deploying a Price Prediction Model to a Cloud Serving Platform Automating and Documenting a Model Deployment Workflow Overnight Churn Prediction Pipeline Integrating a Sentiment Model into the Customer Feedback Platform Production Launch: Price Optimization Model

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