What ML frameworks and libraries does this skill cover?

This skill covers PyTorch 2.x, TensorFlow 2.x/Keras, JAX/Flax, classical ML libraries (scikit-learn, XGBoost, LightGBM, CatBoost), ONNX, Hugging Face Transformers/Accelerate, and Ray/Ray Train for distributed computing.

What model serving and deployment platforms are included?

The skill covers TensorFlow Serving, TorchServe, MLflow, BentoML, Docker, Kubernetes, cloud ML services (AWS SageMaker, Azure ML, GCP Vertex AI, Databricks ML), FastAPI, Flask, gRPC, and edge deployment options like TensorFlow Lite and PyTorch Mobile.

When should I use this skill versus when should I avoid it?

Use this skill for production ML system design, feature stores, distributed training, model monitoring, CI/CD pipelines, and inference optimization. Don't use it for tasks unrelated to ML engineering or for pure research/notebook-stage model exploration without a production deployment goal.

What MLOps and infrastructure practices does this skill cover?

This skill covers end-to-end ML pipelines, continuous training, Infrastructure as Code (Terraform, CloudFormation, Pulumi), model/data drift monitoring, A/B testing, model governance, and monitoring via Prometheus/Grafana.

Skill

Engineer Production-Ready ML Systems

Name: Ml Engineer
Availability: OnlineOnly
Author: Antigravity

ML Engineer skill provides expertise in production-ready machine learning systems using PyTorch 2.x, TensorFlow 2.x, model serving, feature engineering, and ML

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Works with pytorchtensorflowjaxsklearnxgboost

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Spark score

out of 100

Updated 2 days ago

Source checked Sep 19, 2026

Version 17.5.0

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Why it matters

Deploy and manage robust, scalable, and efficient machine learning systems in production environments. This skill focuses on the entire lifecycle from model serving and infrastructure to feature engineering and MLOps.

Outcomes

What it gets done

Design and implement scalable model serving architectures.

Integrate and optimize ML models using core frameworks like PyTorch and TensorFlow.

Establish robust MLOps pipelines for continuous integration and deployment.

Implement comprehensive monitoring, testing, and governance for ML systems.

Install

Add it to your toolbox

Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/ag-ml-engineer | bash

After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.

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Overview

Ml Engineer

This skill provides expert ML engineering capabilities for building production-ready machine learning systems. It covers modern ML frameworks (PyTorch 2.x, TensorFlow 2.x), model serving architectures, feature engineering, and ML infrastructure, with a focus on creating scalable, reliable, and efficient systems that deliver business value in production environments. Use this skill when working with production-ready machine learning systems, modern ML frameworks like PyTorch 2.x or TensorFlow 2.x, model serving architectures, feature engineering, or ML infrastructure. It focuses on scalable, reliable, and efficient ML systems in production environments.

What it does

This skill provides machine learning engineering expertise specializing in production-ready ML systems. It covers modern ML frameworks (PyTorch 2.x, TensorFlow 2.x), model serving architectures, feature engineering, and ML infrastructure, with a focus on scalable, reliable, and efficient ML systems that deliver business value in production environments.

When to use - and when NOT to

Use this skill when working with production-ready machine learning systems, modern ML frameworks like PyTorch 2.x or TensorFlow 2.x, model serving architectures, feature engineering, or ML infrastructure. It focuses on scalable, reliable, and efficient ML systems in production environments.

Do not use this skill for pure research or experimental ML work where production concerns are not relevant, or when you need domain-specific ML expertise outside the scope of general production ML engineering (such as highly specialized computer vision or NLP research).

Inputs and outputs

This skill addresses production-ready machine learning systems, covering modern ML frameworks (PyTorch 2.x, TensorFlow 2.x), model serving architectures, feature engineering, and ML infrastructure, with emphasis on scalability, reliability, and efficiency in production environments.

Integrations

This skill covers PyTorch 2.x and TensorFlow 2.x as modern ML frameworks, along with model serving architectures, feature engineering, and ML infrastructure.

Who it's for

This skill is for those working with production-ready machine learning systems using modern ML frameworks, model serving architectures, feature engineering, and ML infrastructure.

Source README

Expert ML engineer specializing in production-ready machine learning systems. Masters modern ML frameworks (PyTorch 2.x, TensorFlow 2.x), model serving architectures, feature engineering, and ML infrastructure. Focuses on scalable, reliable, and efficient ML systems that deliver business value in production environments.

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