Skill UI

Provides expert guidance on designing, implementing, and optimizing cutting-edge computer vision pipelines. Expertise spans real-time object detection (YOLO26), advanced promptable segmentation (SAM 3), and leveraging Vision Language Models (VLMs) for complex visual reasoning. Ideal for building high-performance systems in robotics, autonomous vehicles, and industrial inspection, including 3D reconstruction and edge deployment optimization.

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.

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.

Automates FastAPI ML endpoint deployments by guiding architecture, producing production-ready code, and validating serving configs when you invoke the ML deployment skill.

Guides ML engineers through dataset prep, PEFT strategy selection, LoRA/QLoRA adapter setup, training monitoring, and deployment checks for practical LLM fine-tuning, quantization, and adapter merge workflows.

Automates Flask-based machine learning API deployment by guiding you through best practices, generating production code/configurations, and validating outputs for serving, monitoring, and MLOps pipelines.

Generates complete, production-ready Google Cloud Platform (GCP) code examples and infrastructure templates. It spans various modern AI patterns, including ADK agents, Genkit RAG flows, and multimodal analysis using Vertex AI and Gemini. The output includes runnable code, deployment configuration (Terraform/IaC), security best practices (IAM, VPC-SC), and monitoring setup, ensuring enterprise-grade reliability and cost optimization.

A comprehensive guide to building enterprise-grade AI applications using Firebase Genkit. This skill covers the full development lifecycle, including implementing advanced RAG systems, multi-step tool-calling agents, and complex workflows across Node.js, Python, and Go. It teaches best practices for schema validation (Zod/Pydantic), robust error handling, OpenTelemetry tracing, and deploying reliably to Firebase Functions or Cloud Run.

Master Google Cloud’s Agent Development Kit and Starter Pack to build containerized multi-agent systems, automate RAG workflows, and deploy production-ready agents via Cloud Run, GKE, Agent Engine, or CI/CD pipelines.

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.

Provides calibration-free weight quantization for LLMs down to 1-bit precision with multiple optimized backends, ideal for fast deployments with vLLM/HuggingFace and LoRA/PEFT fine-tuning without collecting calibration data.

LitGPT provides clean, readable, and production-ready implementations for over 20 state-of-the-art LLMs (including Llama, Gemma, Phi, Mistral). It supports comprehensive workflows, including full fine-tuning, efficient LoRA/QLoRA adaptation, and pretraining from scratch. Ideal for developers needing academic understanding or robust, deployable model architectures.