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Search Model Serving , found 32 results

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Canary Deployment Setup

canary-deployment-setup

jeremylongshore/claude-code-plugins-plus-skills

Provides automated guidance for setting up canary deployments in ML production environments, covering model serving, MLOps pipelines, monitoring, and configuration validation.

Databricks ML Workflow: Training and Serving

databricks-core-workflow-b

jeremylongshore/claude-code-plugins-plus-skills

This guide outlines the complete MLOps workflow on Databricks. It covers feature engineering using Feature Store, tracking experiments with MLflow, managing model versions in the Model Registry, and deploying real-time inference endpoints via Mosaic AI Model Serving. Ideal for building robust, production-grade ML pipelines.

Automated Model Deployment

deploying-machine-learning-models

jeremylongshore/claude-code-plugins-plus-skills

Deploying Machine Learning Models automates serving trained models to production by analyzing requirements, generating API and infrastructure code, and orchestrating deployment workflows with validation, error handling, and monitoring baked in.

GPU Resource Optimizer

gpu-resource-optimizer

jeremylongshore/claude-code-plugins-plus-skills

Automates GPU resource optimization for ML deployments by guiding best practices, generating production-ready configurations, and validating setups for model serving, MLOps pipelines, and monitoring.

Inference Latency Profiler

inference-latency-profiler

jeremylongshore/claude-code-plugins-plus-skills

Automates inference latency profiler tasks in ML deployment scenarios, offering step-by-step guidance on model serving, MLOps pipelines, monitoring, and production optimization, generating production-ready code and validating outputs against best practices.

Production ML Engineering and MLOps

sickn33/antigravity-awesome-skills

A comprehensive skill set for building, deploying, and managing production-grade Machine Learning systems. Expertise spans modern frameworks (PyTorch, TensorFlow), model serving using Kubernetes, advanced feature engineering (Feast, Tecton), and robust MLOps practices (monitoring, A/B testing, CI/CD). Focuses on delivering scalable, reliable, and business-value-driven AI solutions.

Model Registry Manager

model-registry-manager

jeremylongshore/claude-code-plugins-plus-skills

Automates guidance for model registry manager tasks within ML deployment, covering serving, MLOps pipelines, monitoring, and production optimization whenever that trigger phrase appears.

Model Versioning Manager

model-versioning-manager

jeremylongshore/claude-code-plugins-plus-skills

Provides automated guidance for managing model versions in ML deployment, including serving, monitoring, and production optimization whenever someone requests the model versioning manager.

Speculative Decoding Acceleration

speculative-decoding

Orchestra-Research/AI-Research-SKILLs

Speeds up LLM inference by combining speculative decoding with Medusa multiple heads and lookahead techniques so chatbots, code assistants, and other real-time workloads can run with 1.5-3.6× lower latency while preserving target-model quality.

Tensorflow SavedModel Creator

tensorflow-savedmodel-creator

jeremylongshore/claude-code-plugins-plus-skills

Automates TensorFlow SavedModel deployment support with step-by-step guidance, best-practice patterns, production-ready code/config generation, and validation for serving, monitoring, and optimization scenarios.

TensorRT LLM Optimizer

Orchestra-Research/AI-Research-SKILLs

Optimizes large language model inference on NVIDIA GPUs with TensorRT, delivering 10-100× faster throughput, quantized precision (FP8/INT4), multi-GPU scaling, and serving-ready tooling for production deployments.

Torchserve Config Generator

torchserve-config-generator

jeremylongshore/claude-code-plugins-plus-skills

Automates generating TorchServe deployment configurations with guided best practices for model serving, MLOps pipelines, inference, and production monitoring so ML deployment workflows stay consistent and reliable.

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