senior-ml-engineer

To set up MLOps pipelines, you establish automated training, versioning, and deployment workflows that package and deploy ML models using Docker and serving frameworks. This ensures models are productionized with automated rollouts.

The best way to integrate an LLM with a RAG system is by using deployment workflows that handle retries and cost management. This ensures your LLM integration remains robust and controlled within production environments.

Model monitoring works by tracking performance metrics like latency and error rates in production ML environments. If acceptable limits are breached, the system prevents automatic rollouts to ensure stability.

Yes, you can use Docker to package and deploy machine learning models. The process utilizes Docker alongside serving frameworks to move model artifacts from staging into full production environments.

Yes, you need a trained model artifact before starting model deployment. The deployment process takes this existing model artifact, such as one located at a specific directory, and packages it for production.