Skill UI

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.

Automates dataset loader creation within ML Training workflows, offering best-practice guidance, validation checks, and production-ready code to accelerate data prep and model tuning.

A comprehensive guide to implementing advanced LLM patterns using the Groq API. This workflow demonstrates how to handle chat completions, robust tool/function calling for integrating external services (like weather or search), and enforcing structured data output using JSON mode and strict schemas. Ideal for building real-time, production-grade AI features.

Automated guidance for hyperparameter tuning within ML training workflows, offering best practices, production-ready code, and validation across modeling stages like data prep, training, and experiment tracking.

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.

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.

Phoenix is an open-source observability platform for tracing, evaluating, and monitoring LLM applications; use it to debug prompts, run dataset evaluations, build experiment pipelines, and watch production inference in real time.

Fully managed, auto-scaling vector database tailored for production AI workloads such as RAG, recommendations, and semantic search, offering hybrid dense+sparse search, namespaces, metadata filters, and <100ms latency on serverless infrastructure.

Automates PyTorch training workflows by guiding data preparation, model configuration, hyperparameter tuning, and experiment tracking while following industry best practices for production readiness.

Qdrant is a high-performance, Rust-powered vector database designed for production-grade RAG (Retrieval-Augmented Generation) and semantic search applications. It provides fast nearest neighbor search, supports complex hybrid search using metadata filtering, and offers horizontal scalability through sharding and replication. Ideal for building real-time recommendation systems and enterprise-level knowledge retrieval.

Comprehensive skill covering the full lifecycle of visual AI systems. Expertise includes object detection (YOLO, Faster R-CNN), instance and semantic segmentation (SAM, Mask R-CNN), and video analysis. The workflow guides users through dataset preparation, selecting optimal architectures, training using frameworks like PyTorch, and achieving production readiness via model optimization (ONNX, TensorRT) and deployment on various edge or cloud targets.

Guides post-training of GLM-class LLMs with Megatron-LM and SGLang, focusing on GRPO rollout generation, custom data buffers, and tight RL scaling workflows for research and production use.