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home / skills / jeremylongshore / claude-code-plugins-plus-skills / tensorflow-savedmodel-creator

tensorflow-savedmodel-creator skill

/skills/08-ml-deployment/tensorflow-savedmodel-creator

This skill provides automated guidance and production-ready configurations for tensorflow savedmodel creator tasks in ML deployment.

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill tensorflow-savedmodel-creator

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
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---
name: "tensorflow-savedmodel-creator"
description: |
  Create tensorflow savedmodel creator operations. Auto-activating skill for ML Deployment.
  Triggers on: tensorflow savedmodel creator, tensorflow savedmodel creator
  Part of the ML Deployment skill category. Use when working with tensorflow savedmodel creator functionality. Trigger with phrases like "tensorflow savedmodel creator", "tensorflow creator", "tensorflow".
allowed-tools: "Read, Write, Edit, Bash(cmd:*), Grep"
version: 1.0.0
license: MIT
author: "Jeremy Longshore <[email protected]>"
---

# Tensorflow Savedmodel Creator

## Overview

This skill provides automated assistance for tensorflow savedmodel creator tasks within the ML Deployment domain.

## When to Use

This skill activates automatically when you:
- Mention "tensorflow savedmodel creator" in your request
- Ask about tensorflow savedmodel creator patterns or best practices
- Need help with machine learning deployment skills covering model serving, mlops pipelines, monitoring, and production optimization.

## Instructions

1. Provides step-by-step guidance for tensorflow savedmodel creator
2. Follows industry best practices and patterns
3. Generates production-ready code and configurations
4. Validates outputs against common standards

## Examples

**Example: Basic Usage**
Request: "Help me with tensorflow savedmodel creator"
Result: Provides step-by-step guidance and generates appropriate configurations


## Prerequisites

- Relevant development environment configured
- Access to necessary tools and services
- Basic understanding of ml deployment concepts


## Output

- Generated configurations and code
- Best practice recommendations
- Validation results


## Error Handling

| Error | Cause | Solution |
|-------|-------|----------|
| Configuration invalid | Missing required fields | Check documentation for required parameters |
| Tool not found | Dependency not installed | Install required tools per prerequisites |
| Permission denied | Insufficient access | Verify credentials and permissions |


## Resources

- Official documentation for related tools
- Best practices guides
- Community examples and tutorials

## Related Skills

Part of the **ML Deployment** skill category.
Tags: mlops, serving, inference, monitoring, production

Overview

This skill automates creation of TensorFlow SavedModel artifacts for ML deployment workflows. It provides step-by-step guidance, generates production-ready code and configuration, and validates outputs against common standards. Use it to streamline model export, packaging, and serving preparation.

How this skill works

The skill inspects your model code and deployment intent, then generates TensorFlow SavedModel export operations and associated configuration snippets. It follows industry best practices for signatures, asset packing, and versioning, and includes validation checks for common export errors. Outputs include Python export code, CLI commands, and minimal configuration for serving platforms.

When to use it

  • You need to export a trained TensorFlow model to the SavedModel format for serving or packaging.
  • Preparing models for production serving platforms (TF Serving, Kubernetes, cloud services).
  • Creating reproducible export code and reproducible versioned artifacts for CI/CD pipelines.
  • Validating SavedModel structure, signatures, and compatibilities before deployment.

Best practices

  • Define explicit tf.function signatures for predictable serving inputs and outputs.
  • Include model versioning and atomic directory swaps to avoid inconsistent serving states.
  • Bundle preprocessing/postprocessing artifacts alongside the SavedModel when needed.
  • Run automated validation tests that load the SavedModel and compare inference outputs.
  • Use lightweight configuration files to document expected input shapes, dtypes, and export metadata.

Example use cases

  • Generate a Python export script that wraps a Keras model and exports a Signed tf.saved_model with signatures.
  • Create CI pipeline steps to export, validate, and snapshot a SavedModel artifact for deployment.
  • Produce configuration snippets for TensorFlow Serving or cloud model registry integrations.
  • Diagnose export failures by validating saved signatures, assets, and variable checkpoints.
  • Prepare models for A/B rollouts by producing versioned SavedModel directories and deployment instructions.

FAQ

What inputs do you need to generate an export script?

Provide the model object or loading code, desired serving signatures, example inputs for shape inference, and target export directory or versioning scheme.

Can this handle custom layers or preprocessing logic?

Yes — the skill includes guidance to wrap custom components into tf.Module or concrete functions and to serialize necessary assets alongside the SavedModel.