How to copy a dataset into your cluster, deploy an LLM in the cluster, and run your function.

Set up an effective model monitoring system that leverages LLMs to maintain high standards for deployed models.

How to track experiments for document-based models using the LangChain API to integrate directly with vector databases.

How to deploy real-time serving pipelines with MLRun Serving and different types of pre-trained ML/DL models.

How to work with projects, source control (git), CI/CD, to easily build and deploy multi-stage ML pipelines.

Demonstrate MLRun Serving pipelines, MLRun model monitoring, and automated drift detection.

Turn a Kaggle research notebook to a production ML micro-service with minimal code changes using MLRun.

Understand MLRun feature store with a simple example: build, transform, and serve features in batch and in real-time.

Use MLRun batch inference function (from MLRun Function Hub), run it as a batch job, and generate drift reports.

Demonstrates a multi-step online pipeline with data prep, ensemble, model serving, and post processing.

Use the feature store with data ingestion, model training, model serving, and automated pipeline.