AI/ML Operations

What problem does it solve?

This Skill helps data teams implement and maintain ML operations workflows, including deployment, serving, monitoring, and governance of machine learning models.

Core Features & Use Cases

• End-to-end MLOps: from data ingestion and feature engineering to training, evaluation, registry, and serving.
• Model Serving & Inference: quick start with TensorFlow Serving or Triton to deploy models as scalable endpoints.
• Model Monitoring & Governance: track drift, performance, and retraining triggers; manage model versions with a registry.
• Reference Implementations: examples for MLflow experiments, Feast feature stores, and GPU-enabled infrastructure.

Quick Start

Start by launching a TensorFlow Serving container for a sample model and sending a test request.