Skill UI

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

Client library to manage Azure Machine Learning resources—workspaces, datasets, compute, jobs, models, environments, and pipelines—directly from Python with MLClient authentication, configuration, and best practices for reproducible workflows.

Client library to manage Azure Machine Learning resources—workspaces, datasets, compute, jobs, models, environments, and pipelines—directly from Python with MLClient authentication, configuration, and best practices for reproducible workflows.

Builds AutoML pipelines by analyzing user requests, generating code for data preparation, model selection, hyperparameter tuning, validation, and metrics, then documenting artifacts for streamlined automated machine learning workflows.

Build a minimal Customer.io integration that identifies a user, triggers an event, and validates success on the dashboard—ideal for learning the SDK, testing credentials, or creating your first messaging workflow.

Provides expert guidance on advanced analytics, machine learning, and statistical modeling, covering the full data science workflow from exploratory analysis to production-ready models and stakeholder reporting.

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.

Guides researchers through drafting publication-ready machine learning papers for NeurIPS, ICML, ICLR, ACL, AAAI, and COLM by combining expert writing philosophy with LaTeX templates, citation verification APIs, and collaborative workflows.

nanoGPT is a simplified, minimalist implementation designed specifically for educational purposes. It reproduces the core architecture of GPT-2 (124M) using clean, hackable code, allowing users to understand the entire transformer pipeline from scratch. The framework supports full workflows, including data preparation on various datasets (e.g., Shakespeare, OpenWebText), model training, and text generation, making it ideal for students and researchers learning NLP and deep learning principles.

Provides a minimal PostHog SDK example for initializing the client and making the first API call, useful when integrating a new project, validating credentials, or learning the basic workflow in TypeScript or Python.

Prediction Monitor Assistant automates machine learning deployment monitoring tasks, offering step-by-step guidance, best-practice patterns, production-ready code and configuration generation, and validation to keep serving and inference workflows reliable.