senior-ml-engineer

What problem does it solve?

This Skill streamlines the process of deploying machine learning models into production environments and establishing robust MLOps pipelines, addressing the complexities of model lifecycle management.

Core Features & Use Cases

• Model Deployment: Guides users through containerizing models (Docker) and deploying them with strategies like canary releases.
• MLOps Pipeline Setup: Facilitates the creation of automated training, registration, and deployment workflows using tools like MLflow and feature stores.
• LLM Integration: Provides patterns for integrating Large Language Models with retry logic, cost control, and structured output.
• RAG Systems: Details the implementation of Retrieval Augmented Generation pipelines, including vector database selection and document chunking.
• Model Monitoring: Covers setting up drift detection, performance monitoring, and alerting for production models.
• Use Case: A user needs to deploy a trained recommendation model to production, set up continuous monitoring for data drift, and integrate it with an existing API.

Quick Start

Use the senior-ml-engineer skill to deploy a model using a FastAPI Uvicorn container.