> ## Documentation Index
> Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Course: Fundamentals of MLOps

> This course introduces MLOps principles, practical labs, model deployment, compliance, and community engagement for aspiring MLOps professionals.

Notes for the **Fundamentals of MLOps** course: 49 lessons across 7 sections.

## Introduction to MLOps

* [Course Introduction](/docs/Fundamentals-of-MLOps/Introduction-to-MLOps/Course-Introduction/page.md)
* [Getting Started with Machine Learning Team](/docs/Fundamentals-of-MLOps/Introduction-to-MLOps/Getting-Started-with-Machine-Learning-Team/page.md)
* [Introducing MLOps Engineer](/docs/Fundamentals-of-MLOps/Introduction-to-MLOps/Introducing-MLOps-Engineer/page.md)
* [DevOps and MLOps A Comparison](/docs/Fundamentals-of-MLOps/Introduction-to-MLOps/DevOps-and-MLOps-A-Comparison/page.md)
* [MLOps LifeCycle](/docs/Fundamentals-of-MLOps/Introduction-to-MLOps/MLOps-LifeCycle/page.md)
* [Continuous Integration CI Continuous Deployment CD Continuous Training CT Continuous Monitoring CM](/docs/Fundamentals-of-MLOps/Introduction-to-MLOps/Continuous-Integration-CI-Continuous-Deployment-CD-Continuous-Training-CT-Continuous-Monitoring-CM/page.md)
* [Finding and Exploring Right Tools from DevOps for MLOps](/docs/Fundamentals-of-MLOps/Introduction-to-MLOps/Finding-and-Exploring-Right-Tools-from-DevOps-for-MLOps/page.md)
* [MLOps Architecture](/docs/Fundamentals-of-MLOps/Introduction-to-MLOps/MLOps-Architecture/page.md)

## Data Collection and Preparation

* [Data Collection and Preparation](/docs/Fundamentals-of-MLOps/Data-Collection-and-Preparation/Data-Collection-and-Preparation/page.md)
* [Data Ingestion ETL](/docs/Fundamentals-of-MLOps/Data-Collection-and-Preparation/Data-Ingestion-ETL/page.md)
* [Idea of Data Lake](/docs/Fundamentals-of-MLOps/Data-Collection-and-Preparation/Idea-of-Data-Lake/page.md)
* [Data Cleaning and Data Transformation](/docs/Fundamentals-of-MLOps/Data-Collection-and-Preparation/Data-Cleaning-and-Data-Transformation/page.md)
* [Small to Medium Datasets Data Transformation Pandas Polars](/docs/Fundamentals-of-MLOps/Data-Collection-and-Preparation/Small-to-Medium-Datasets-Data-Transformation-Pandas-Polars/page.md)
* [Demo Small to Medium Datasets Data Transformation Pandas Polars](/docs/Fundamentals-of-MLOps/Data-Collection-and-Preparation/Demo-Small-to-Medium-Datasets-Data-Transformation-Pandas-Polars/page.md)
* [Large Datasets Apache Spark PySpark Dask](/docs/Fundamentals-of-MLOps/Data-Collection-and-Preparation/Large-Datasets-Apache-Spark-PySpark-Dask/page.md)
* [Streaming Datasets Apache Kafka Apache Flink](/docs/Fundamentals-of-MLOps/Data-Collection-and-Preparation/Streaming-Datasets-Apache-Kafka-Apache-Flink/page.md)
* [Demo Stream Data using Apache Kafka](/docs/Fundamentals-of-MLOps/Data-Collection-and-Preparation/Demo-Stream-Data-using-Apache-Kafka/page.md)
* [What is Feature Store](/docs/Fundamentals-of-MLOps/Data-Collection-and-Preparation/What-is-Feature-Store/page.md)
* [Data Pipeline Orchestration Airflow Perfect](/docs/Fundamentals-of-MLOps/Data-Collection-and-Preparation/Data-Pipeline-Orchestration-Airflow-Perfect/page.md)
* [Demo Data Pipeline Orchestration](/docs/Fundamentals-of-MLOps/Data-Collection-and-Preparation/Demo-Data-Pipeline-Orchestration/page.md)

## Model Development and Training

* [Model Development](/docs/Fundamentals-of-MLOps/Model-Development-and-Training/Model-Development/page.md)
* [Model Training and Hyperparameter tuning](/docs/Fundamentals-of-MLOps/Model-Development-and-Training/Model-Training-and-Hyperparameter-tuning/page.md)
* [World of CPUs and GPUs](/docs/Fundamentals-of-MLOps/Model-Development-and-Training/World-of-CPUs-and-GPUs/page.md)
* [Introduction to MLflow](/docs/Fundamentals-of-MLOps/Model-Development-and-Training/Introduction-to-MLflow/page.md)
* [Demo Setting up MLflow](/docs/Fundamentals-of-MLOps/Model-Development-and-Training/Demo-Setting-up-MLflow/page.md)
* [Demo Running and experiment and storing the result on MLflow](/docs/Fundamentals-of-MLOps/Model-Development-and-Training/Demo-Running-and-experiment-and-storing-the-result-on-MLflow/page.md)
* [Demo MLflow Model Artifact and Versioning](/docs/Fundamentals-of-MLOps/Model-Development-and-Training/Demo-MLflow-Model-Artifact-and-Versioning/page.md)

## Model Deployment and Serving

* [Model Serving](/docs/Fundamentals-of-MLOps/Model-Deployment-and-Serving/Model-Serving/page.md)
* [Model Drift and OnlineOffline Serving](/docs/Fundamentals-of-MLOps/Model-Deployment-and-Serving/Model-Drift-and-OnlineOffline-Serving/page.md)
* [Model Deployment and Serving](/docs/Fundamentals-of-MLOps/Model-Deployment-and-Serving/Model-Deployment-and-Serving/page.md)
* [Demo Model Serving using BentoML](/docs/Fundamentals-of-MLOps/Model-Deployment-and-Serving/Demo-Model-Serving-using-BentoML/page.md)
* [Demo Upgrading Model Versions with BentoML Serving](/docs/Fundamentals-of-MLOps/Model-Deployment-and-Serving/Demo-Upgrading-Model-Versions-with-BentoML-Serving/page.md)
* [Monitoring Tools PrometheusGrafanaEvidently](/docs/Fundamentals-of-MLOps/Model-Deployment-and-Serving/Monitoring-Tools-PrometheusGrafanaEvidently/page.md)

## Automating Insurance Claim Reviews with MLflow and BentoML

* [Deploy App for Insurance Agents to Upload all Insurance Claims](/docs/Fundamentals-of-MLOps/Automating-Insurance-Claim-Reviews-with-MLflow-and-BentoML/Deploy-App-for-Insurance-Agents-to-Upload-all-Insurance-Claims/page.md)
* [Demo Generate Dummy Data for the Project](/docs/Fundamentals-of-MLOps/Automating-Insurance-Claim-Reviews-with-MLflow-and-BentoML/Demo-Generate-Dummy-Data-for-the-Project/page.md)
* [Demo Setup MLflow server and run the ML Experiment](/docs/Fundamentals-of-MLOps/Automating-Insurance-Claim-Reviews-with-MLflow-and-BentoML/Demo-Setup-MLflow-server-and-run-the-ML-Experiment/page.md)
* [Demo Register the Model and Setup BentoML for Serving ML Models](/docs/Fundamentals-of-MLOps/Automating-Insurance-Claim-Reviews-with-MLflow-and-BentoML/Demo-Register-the-Model-and-Setup-BentoML-for-Serving-ML-Models/page.md)
* [Demo Upgrade Python Flask App to Connect to BentoML for Online Serving](/docs/Fundamentals-of-MLOps/Automating-Insurance-Claim-Reviews-with-MLflow-and-BentoML/Demo-Upgrade-Python-Flask-App-to-Connect-to-BentoML-for-Online-Serving/page.md)

## Data Security and Governance

* [Data Privacy and Data Security](/docs/Fundamentals-of-MLOps/Data-Security-and-Governance/Data-Privacy-and-Data-Security/page.md)
* [Data Access Management](/docs/Fundamentals-of-MLOps/Data-Security-and-Governance/Data-Access-Management/page.md)
* [Data Retention](/docs/Fundamentals-of-MLOps/Data-Security-and-Governance/Data-Retention/page.md)
* [Need of Compliance and GDPR](/docs/Fundamentals-of-MLOps/Data-Security-and-Governance/Need-of-Compliance-and-GDPR/page.md)
* [HIPAA Compliance](/docs/Fundamentals-of-MLOps/Data-Security-and-Governance/HIPAA-Compliance/page.md)
* [PCI Compliance](/docs/Fundamentals-of-MLOps/Data-Security-and-Governance/PCI-Compliance/page.md)
* [Compliance Consequences and Penalties](/docs/Fundamentals-of-MLOps/Data-Security-and-Governance/Compliance-Consequences-and-Penalties/page.md)
* [Compliance Summary](/docs/Fundamentals-of-MLOps/Data-Security-and-Governance/Compliance-Summary/page.md)

## Sneak Peek into AWS SageMaker

* [Overview of SageMaker](/docs/Fundamentals-of-MLOps/Sneak-Peek-into-AWS-SageMaker/Overview-of-SageMaker/page.md)
* [Core Components of SageMaker](/docs/Fundamentals-of-MLOps/Sneak-Peek-into-AWS-SageMaker/Core-Components-of-SageMaker/page.md)
* [MLOps with SageMaker](/docs/Fundamentals-of-MLOps/Sneak-Peek-into-AWS-SageMaker/MLOps-with-SageMaker/page.md)


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