Skill UI

Defines how to build AI wrapper products that solve specific customer problems, covering prompt engineering, API cost control, usage metering, and defensible UX so AI becomes a paid, reliable feature.

Defines how to build AI wrapper products that solve specific customer problems, covering prompt engineering, API cost control, usage metering, and defensible UX so AI becomes a paid, reliable feature.

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.

Feature Engineering Toolkit automates creating, selecting, and transforming dataset features to improve machine learning models; use it whenever you need new interactions, automatic selection, scaling, or encoding to boost accuracy and interpretability.

Automatically activates when you mention feature engineering helper, offering guidance on data preparation, model training, hyperparameter tuning, and experiment tracking for ML projects.

This skill guides the design and implementation of a complete, production-grade Machine Learning pipeline. It employs a multi-agent orchestration workflow, systematically covering all stages of the ML lifecycle: data ingestion and quality assurance, feature engineering, model training, optimization, and robust deployment (MLOps). It ensures built-in reproducibility, monitoring, and scalability.

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.

This skill provides comprehensive guidance for building production-grade MLOps pipelines. It covers the entire machine learning lifecycle, from initial data ingestion and rigorous feature engineering to model training, advanced validation (A/B testing, performance drift), and robust deployment strategies (Canary, Blue-green). It enables the design and orchestration of scalable, reproducible, and observable ML systems using tools like Airflow and Kubeflow.

A comprehensive companion tool for the foundational Deep Learning textbook. It indexes 20 chapters and provides a crucial delta layer, updating core concepts from 2016 to 2026 (e.g., Transformers, Diffusion Models). The tool features four deterministic utilities: a reading-path planner, a training-failure diagnostic, a capacity planner, and a parameter/FLOP calculator. Ideal for students and practitioners who need to study the theory, understand its modern relevance, and translate academic knowledge into actionable engineering decisions.

Design and evaluate LLM product features, retrieval, validated outputs, latency, and cost controls. Covers LLM integration patterns, RAG architecture, scalable prompt engineering, trustworthy AI UX, and cost optimization strategies for sustainable AI implementation in production environments.

This skill offers a design and workflow guide for intelligent context management within AI workflows. It focuses on capturing project state, enabling semantic retrieval, and supporting multi-agent coordination. Key features include state serialization, compression techniques, vector database integration, and knowledge graph construction. It helps preserve architectural decisions and facilitate knowledge transfer without implementing actual storage functions, serving as a strategic planning tool for context engineering.

Design and implement production-ready ML pipelines using multi-agent MLOps orchestration, covering data engineering, feature engineering, model training, experiment tracking, deployment, and serving.