Skill UI

A comprehensive checklist for ensuring GPU workloads, such as inference services or model training pipelines, are fully prepared for production deployment on CoreWeave. It covers critical MLOps and DevOps aspects including autoscaling, resource limits, security policies, persistent storage validation, monitoring setup, and rollback procedures.

This reference architecture provides a comprehensive blueprint for deploying Machine Learning models on CoreWeave's GPU cloud infrastructure. It details the structure for multi-model serving (e.g., vLLM, TGI), scalable Kubernetes deployments, shared persistent storage (PVC), and autoscaling mechanisms using KServe/Knative. Use this when designing robust MLOps pipelines, planning high-performance multi-model inference services, or establishing standardized best practices for GPU cloud deployment.

This skill generates standardized, production-ready `mise.toml` configurations. It helps developers standardize runtime versions across mixed-language projects (e.g., Node.js, Python, Go, etc.) and CI/CD pipelines, eliminating the need for multiple legacy version managers like asdf or nvm. Ideal for ensuring reproducible, consistent development environments.

An advanced AI tool utilizing Meta's Segment Anything 2 (SAM2) model for interactive, pixel-perfect object segmentation. Users can click on any object within a frame or video to automatically generate precise masks. Key features include point/box prompting, video tracking across multiple frames, and seamless integration into Annotation Studio workflows, making it ideal for large-scale data labeling and video analysis pipelines.

This guide provides a comprehensive setup for Continuous Integration/Continuous Deployment (CI/CD) pipelines for Shopify apps. It utilizes GitHub Actions to automate linting, unit testing, and critical integration tests. Key features include API version compatibility checking, secure secret management, and streamlined deployment via the Shopify CLI, ensuring stable and reliable e-commerce functionality. Ideal for setting up automated release cycles or monitoring API changes.

This utility handles asynchronous webhook events from Together AI, enabling developers to build robust, event-driven Machine Learning pipelines. Instead of constantly polling the API for status updates (e.g., fine-tuning completion, batch job status, model deprecation), you can subscribe to these webhooks. It includes logic for signature verification, event routing, and idempotency, making it ideal for mission-critical AI workflows.

This catalog provides comprehensive infrastructure abstractions and hooks for the Webiny ecosystem. It includes standardized lifecycle hooks (Before/After Build/Deploy/Watch) and specialized services (Logger, UI, AWS interactions) for various modules (Admin, API, Core). Use these standardized interfaces to seamlessly integrate custom logic into your build and deployment pipelines, ensuring modularity and consistency.

A comprehensive guide detailing advanced security threats in Machine Learning and AI systems. Covers model supply chain attacks (RCE via pickle, poisoning), adversarial examples (FGSM, PGD, physical-world), data privacy breaches, and LLM-specific vulnerabilities. Essential for building robust, secure AI pipelines.

A comprehensive workflow designed to transition machine learning models from experimental notebooks to reliable, production-grade systems. It enforces best practices including data contract management, reproducible training pipelines, rigorous model evaluation gates, secure deployment strategies, and continuous monitoring/rollback capabilities. Essential for any team moving beyond proof-of-concept ML.