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home / skills / jeremylongshore / claude-code-plugins-plus-skills / tensorflow-serving-setup

tensorflow-serving-setup skill

/skills/08-ml-deployment/tensorflow-serving-setup

This skill guides you through tensorflow serving setup with production-ready configurations, best practices, and validation.

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill tensorflow-serving-setup

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

# Tensorflow Serving Setup

## Overview

This skill provides automated assistance for tensorflow serving setup tasks within the ML Deployment domain.

## When to Use

This skill activates automatically when you:
- Mention "tensorflow serving setup" in your request
- Ask about tensorflow serving setup 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 serving setup
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 serving setup"
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 the setup and configuration of TensorFlow Serving for production ML deployments. It guides you through environment preparation, model packaging, serving configuration, and validation. The skill focuses on reproducible, production-ready outputs that integrate with CI/CD and monitoring stacks.

How this skill works

The skill inspects your project structure and environment requirements, then generates step-by-step instructions, Docker/Kubernetes manifests, and TensorFlow Serving configuration files. It validates common configuration items, flags missing dependencies, and suggests security and performance tuning options. Outputs include runnable examples and checks against common deployment standards.

When to use it

  • You need a repeatable TensorFlow Serving deployment for development or production.
  • You want generated Dockerfiles, Kubernetes manifests, or systemd units for serving models.
  • You need guidance on model versioning, batching, and resource sizing.
  • You want to integrate serving into CI/CD pipelines or observability stacks.
  • You need validation of serving configs and quick troubleshooting steps.

Best practices

  • Package each model version as a self-contained export following TensorFlow SavedModel conventions.
  • Use container images and image tags that match your CI/CD artifact workflow for reproducibility.
  • Enable model versioning and health checks; configure traffic routing or canary rollouts for updates.
  • Configure batching and set resource requests/limits based on load tests to avoid OOM or throttling.
  • Integrate logs, metrics (Prometheus), and tracing to monitor latency and error rates in production.

Example use cases

  • Generate a Dockerfile and docker-compose service for local testing of TensorFlow Serving.
  • Create Kubernetes Deployment, Service, and HorizontalPodAutoscaler manifests for production serving.
  • Produce sample CI pipeline steps to build, test, and push model-serving images with automated validation.
  • Provide configuration and scripts to enable batching, warmup requests, and GPU resource allocation.
  • Audit an existing TensorFlow Serving setup and generate a remediation checklist for performance and security.

FAQ

What prerequisites are required?

A development environment with Docker and kubectl (for K8s), Python tooling for model export, and access to the target cluster or host.

Can the generated configs support GPUs?

Yes. The skill produces Docker and Kubernetes specs that include GPU resource requests and driver compatibility notes when GPU usage is detected or requested.

How does it validate outputs?

It checks for required fields in manifests, basic syntax, common runtime dependencies, and recommends tests such as health-check endpoints and sample inference runs.