# KodeKloud: Beta Courses Fundamentals of MLOps

## Fundamentals of MLOps

### Introduction to MLOps

- [Course Introduction](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Introduction-to-MLOps/Course-Introduction/page.md): This course introduces MLOps principles, practical labs, model deployment, compliance, and community engagement for aspiring MLOps professionals.
- [Getting Started with Machine Learning Team](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Introduction-to-MLOps/Getting-Started-with-Machine-Learning-Team/page.md): This guide explores the structure and roles within machine learning teams, focusing on their contributions and deployment challenges.
- [Introducing MLOps Engineer](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Introduction-to-MLOps/Introducing-MLOps-Engineer/page.md): This article explores the role and responsibilities of an MLOps Engineer, highlighting their importance in bridging data science and production operations.
- [DevOps and MLOps A Comparison](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Introduction-to-MLOps/DevOps-and-MLOps-A-Comparison/page.md): This article compares DevOps and MLOps, highlighting their methodologies, tools, and focus areas in software and machine learning development.
- [MLOps LifeCycle](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Introduction-to-MLOps/MLOps-LifeCycle/page.md): This article explores the essential phases of the MLOps lifecycle to optimize machine learning projects from data exploration to deployment.
- [Continuous Integration CI Continuous Deployment CD Continuous Training CT Continuous Monitoring CM](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Introduction-to-MLOps/Continuous-Integration-CI-Continuous-Deployment-CD-Continuous-Training-CT-Continuous-Monitoring-CM/page.md): This article provides a comprehensive guide on Continuous Integration, Continuous Deployment, Continuous Training, and Continuous Monitoring in MLOps for efficient machine learning operations.
- [Finding and Exploring Right Tools from DevOps for MLOps](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Introduction-to-MLOps/Finding-and-Exploring-Right-Tools-from-DevOps-for-MLOps/page.md): This lesson explores essential DevOps tools that enhance MLOps pipelines, focusing on deployment, management, and scaling of machine learning models for automation and efficiency.
- [MLOps Architecture](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Introduction-to-MLOps/MLOps-Architecture/page.md): This guide explores the critical stages of an MLOps workflow and how each component integrates into a seamless process for machine learning projects.

### Data Collection and Preparation

- [Data Collection and Preparation](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Data-Collection-and-Preparation/Data-Collection-and-Preparation/page.md): This article explores strategies for collecting, cleaning, and consolidating data to build personalized machine learning models for applications like grocery apps.
- [Data Ingestion ETL](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Data-Collection-and-Preparation/Data-Ingestion-ETL/page.md): This article covers data ingestion and the ETL process, emphasizing the importance of integrating data from various sources for analytics and machine learning.
- [Idea of Data Lake](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Data-Collection-and-Preparation/Idea-of-Data-Lake/page.md): This article explores the concept of a data lake, its features, benefits, and its role in modern data architecture and machine learning applications.
- [Data Cleaning and Data Transformation](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Data-Collection-and-Preparation/Data-Cleaning-and-Data-Transformation/page.md): This article covers data cleaning and transformation processes essential for improving data quality and consistency for analysis and machine learning.
- [Demo Small to Medium Datasets Data Transformation Pandas Polars](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Data-Collection-and-Preparation/Demo-Small-to-Medium-Datasets-Data-Transformation-Pandas-Polars/page.md): This hands-on tutorial demonstrates data transformation with Pandas, covering data import, quality checks, cleaning, and preparation for machine learning tasks.
- [Large Datasets Apache Spark PySpark Dask](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Data-Collection-and-Preparation/Large-Datasets-Apache-Spark-PySpark-Dask/page.md): This article focuses on using Apache Spark for processing large datasets through distributed data processing techniques.
- [Streaming Datasets Apache Kafka Apache Flink](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Data-Collection-and-Preparation/Streaming-Datasets-Apache-Kafka-Apache-Flink/page.md): This guide explores how Apache Kafka and Apache Flink work together to manage and process real-time data streams for efficient data ingestion and analysis.
- [Demo Stream Data using Apache Kafka](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Data-Collection-and-Preparation/Demo-Stream-Data-using-Apache-Kafka/page.md): This article provides a hands-on demonstration of setting up Apache Kafka for real-time data streaming using Docker and Python.
- [What is Feature Store](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Data-Collection-and-Preparation/What-is-Feature-Store/page.md): This article explores feature stores and their advantages over traditional data lakes for real-time machine learning applications in a flight booking service.
- [Data Pipeline Orchestration Airflow Perfect](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Data-Collection-and-Preparation/Data-Pipeline-Orchestration-Airflow-Perfect/page.md): This article explores data pipeline orchestration using tools like Airflow and Prefect to automate, monitor, and manage complex workflows efficiently.
- [Demo Data Pipeline Orchestration](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Data-Collection-and-Preparation/Demo-Data-Pipeline-Orchestration/page.md): This article provides a guide on orchestrating data pipelines using Apache Airflow in a factory IoT setting.

### Model Development and Training

- [Model Development](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Model-Development-and-Training/Model-Development/page.md): This article covers the process of model development and training in machine learning, highlighting the roles of data scientists and MLOps engineers.
- [Model Training and Hyperparameter tuning](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Model-Development-and-Training/Model-Training-and-Hyperparameter-tuning/page.md): This article covers essential steps and best practices for optimizing machine learning models through training and hyperparameter tuning.
- [World of CPUs and GPUs](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Model-Development-and-Training/World-of-CPUs-and-GPUs/page.md): This article explores the differences between CPUs and GPUs, their strengths, and their roles in machine learning and computational tasks.
- [Introduction to MLflow](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Model-Development-and-Training/Introduction-to-MLflow/page.md): This article provides an overview of MLflow, a platform for managing the lifecycle of machine learning projects, including tracking, packaging, and deploying models.
- [Demo Setting up MLflow](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Model-Development-and-Training/Demo-Setting-up-MLflow/page.md): This guide explains how to set up an MLflow server in a local development environment using Visual Studio Code.
- [Demo Running and experiment and storing the result on MLflow](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Model-Development-and-Training/Demo-Running-and-experiment-and-storing-the-result-on-MLflow/page.md): This article demonstrates running a local experiment with MLflow, logging metrics, and analyzing results using scikit-learn.
- [Demo MLflow Model Artifact and Versioning](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Model-Development-and-Training/Demo-MLflow-Model-Artifact-and-Versioning/page.md): This lesson covers storing and managing machine learning models in the MLflow Model Artifact Registry using a decision tree regressor as an example.

### Model Deployment and Serving

- [Model Serving](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Model-Deployment-and-Serving/Model-Serving/page.md): This guide covers model deployment and serving in machine learning applications, focusing on integration, microservices architecture, and collaboration between engineers.
- [Model Drift and OnlineOffline Serving](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Model-Deployment-and-Serving/Model-Drift-and-OnlineOffline-Serving/page.md): This article discusses model drift and compares online and offline serving strategies in production machine learning systems.
- [Model Deployment and Serving](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Model-Deployment-and-Serving/Model-Deployment-and-Serving/page.md): This article explores frameworks for deploying machine learning models in production, focusing on TensorFlow Serving, TorchServe, and BentoML for various application needs.
- [Demo Model Serving using BentoML](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Model-Deployment-and-Serving/Demo-Model-Serving-using-BentoML/page.md): This article provides a guide on serving machine learning models using the BentoML platform, including deployment and API creation for a house price prediction model.
- [Demo Upgrading Model Versions with BentoML Serving](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Model-Deployment-and-Serving/Demo-Upgrading-Model-Versions-with-BentoML-Serving/page.md): This tutorial explains how to upgrade machine learning model versions using BentoML while minimizing disruption to live user traffic.
- [Monitoring Tools PrometheusGrafanaEvidently](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Model-Deployment-and-Serving/Monitoring-Tools-PrometheusGrafanaEvidently/page.md): This article explores effective monitoring of machine learning models using tools like Prometheus, Grafana, and Evidently to ensure performance and reliability.

### Automating Insurance Claim Reviews with MLflow and BentoML

- [Deploy App for Insurance Agents to Upload all Insurance Claims](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Automating-Insurance-Claim-Reviews-with-MLflow-and-BentoML/Deploy-App-for-Insurance-Agents-to-Upload-all-Insurance-Claims/page.md): This guide demonstrates integrating an ML pipeline into the insurance claims process to automate and optimize claim handling.
- [Demo Generate Dummy Data for the Project](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Automating-Insurance-Claim-Reviews-with-MLflow-and-BentoML/Demo-Generate-Dummy-Data-for-the-Project/page.md): This guide explains how to generate a synthetic healthcare claims dataset for analysis, including setup, data creation, and export to CSV.
- [Demo Setup MLflow server and run the ML Experiment](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Automating-Insurance-Claim-Reviews-with-MLflow-and-BentoML/Demo-Setup-MLflow-server-and-run-the-ML-Experiment/page.md): This guide covers setting up an MLflow server and running a machine learning experiment for anomaly detection using synthetic health insurance claims data.
- [Demo Register the Model and Setup BentoML for Serving ML Models](https://notes.kodekloud.com/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): This tutorial covers registering a machine learning model with BentoML and deploying a service for predictions.
- [Demo Upgrade Python Flask App to Connect to BentoML for Online Serving](https://notes.kodekloud.com/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): This tutorial explains how to integrate a Flask app with BentoML for online prediction serving using CSV file uploads.

### Data Security and Governance 

- [Data Privacy and Data Security](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Data-Security-and-Governance/Data-Privacy-and-Data-Security/page.md): This article explores data privacy and security principles, best practices, real-world examples, and strategies to protect personal and organizational data.
- [Data Access Management](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Data-Security-and-Governance/Data-Access-Management/page.md): This lesson covers Data Access Management, focusing on secure and efficient data access through automated processes, governance, and continuous monitoring.
- [Data Retention](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Data-Security-and-Governance/Data-Retention/page.md): This article covers key principles and strategies for establishing a robust data retention framework, focusing on compliance, security, collaboration, and efficient data management.
- [Need of Compliance and GDPR](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Data-Security-and-Governance/Need-of-Compliance-and-GDPR/page.md): This article discusses data compliance standards, focusing on GDPR and its importance in protecting individual rights and ensuring proper data handling by organizations.
- [HIPAA Compliance](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Data-Security-and-Governance/HIPAA-Compliance/page.md): This article provides a comprehensive guide on HIPAA compliance, detailing its core components, requirements, and importance in protecting patient health information.
- [PCI Compliance](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Data-Security-and-Governance/PCI-Compliance/page.md): This article provides an in-depth look at PCI compliance, its importance, benefits, consequences of non-compliance, and relevance for MLOps engineers.
- [Compliance Consequences and Penalties](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Data-Security-and-Governance/Compliance-Consequences-and-Penalties/page.md): This article discusses the financial penalties and regulatory scrutiny resulting from non-compliance with data protection regulations, highlighting landmark cases involving Meta, Uber, and WhatsApp.
- [Compliance Summary](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Data-Security-and-Governance/Compliance-Summary/page.md): This article provides an overview of GDPR, HIPAA, and PCI compliance, including violations, penalties, and consequences of non-compliance.

### Sneak Peek into AWS SageMaker

- [Overview of SageMaker](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Sneak-Peek-into-AWS-SageMaker/Overview-of-SageMaker/page.md): This article explores AWS SageMaker and how it streamlines the machine learning lifecycle while addressing common MLOps challenges.
- [Core Components of SageMaker](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Sneak-Peek-into-AWS-SageMaker/Core-Components-of-SageMaker/page.md): This article explores the core components of AWS SageMaker that streamline the MLOps lifecycle and support machine learning workflows.
- [MLOps with SageMaker](https://notes.kodekloud.com/docs/Fundamentals-of-MLOps/Sneak-Peek-into-AWS-SageMaker/MLOps-with-SageMaker/page.md): This article provides a guide on implementing an MLOps lifecycle using AWS SageMaker, covering data processing, model training, and deployment options.