(tutorials-all)= # Tutorials and Examples The MLRun tutorials provide a hands-on introduction to using MLRun to implement data science workflows and to automate both gen AI and machine-learning operations (MLOps) tasks. **In this section** - [**Gen AI tutorials**](#gen-ai-tutorials) - [**Machine learning tutorials**](#other-tutorial) - [**End to End Demos**](#e2e-demos)

(quick-start-tutorial)= ````{card} Make sure you start with the Quick start tutorial to understand the basics ```{button-link} ./01-mlrun-basics.html :color: primary :shadow: :expand: :click-parent: Introduction to MLRun - Use serverless functions to train and deploy models ``` ```` Each of the following tutorials is a dedicated Jupyter notebook. You can download them by clicking the download icon at the top of each page. ```{toctree} :maxdepth: 1 :hidden: genai_01_basic_tutorial genai-02-model-monitor-llm genai-03-vector-db ml-index demos /cheat-sheet ``` ## Gen AI tutorials `````{div} ````{grid} 2 :gutter: 2 ```{grid-item-card} Deploy LLM using MLRun :link: ./genai_01_basic_tutorial.html How to copy a dataset into your cluster, deploy an LLM in the cluster, and run your function. ``` ```{grid-item-card} Model monitoring using LLM :link: ./genai-02-model-monitor-llm.html Set up an effective model monitoring system that leverages LLMs to maintain high standards for deployed models. ``` ```{grid-item-card} Experiment tracking with vector DBs :link: ./genai-03-vector-db.html How to track experiments for document-based models using the LangChain API to integrate directly with vector databases. ``` ```` ````` (other-tutorial)= ## Machine learning tutorials `````{div} ````{grid} 3 :gutter: 2 ```{grid-item-card} Serving pre-trained ML/DL models :link: ./03-model-serving.html How to deploy real-time serving pipelines with MLRun Serving and different types of pre-trained ML/DL models. ``` ```{grid-item-card} Projects & automated ML pipeline :link: ./04-pipeline.html How to work with projects, source control (git), CI/CD, to easily build and deploy multi-stage ML pipelines. ``` ```{grid-item-card} Real-time monitoring & drift detection :link: ./05-model-monitoring.html Demonstrate MLRun Serving pipelines, MLRun model monitoring, and automated drift detection. ``` ```{grid-item-card} Add MLOps to existing code :link: ./07-add-mlops-to-code.html Turn a Kaggle research notebook to a production ML micro-service with minimal code changes using MLRun. ``` ```{grid-item-card} Basic feature store example (stocks) :link: ../feature-store/basic-demo.html Understand MLRun feature store with a simple example: build, transform, and serve features in batch and in real-time. ``` ```{grid-item-card} Batch inference and drift detection :link: ./06-batch-infer.html Use MLRun batch inference function (from MLRun Function Hub), run it as a batch job, and generate drift reports. ``` ```{grid-item-card} Advanced real-time pipeline :link: ../serving/graph-example.html Demonstrates a multi-step online pipeline with data prep, ensemble, model serving, and post processing. ``` ```{grid-item-card} Feature store end-to-end demo :link: ../feature-store/end-to-end-demo/index.html Use the feature store with data ingestion, model training, model serving, and automated pipeline. ``` ```` ````` (e2e-demos)= ## End to end demos See {ref}`demos`. ## Cheat sheet If you already know the basics, use the [cheat sheet](../cheat-sheet.md) as a guide to typical use cases and their flows/SDK.