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Training
Course Information
Course Name
MLops-R: MLOps Using R and MLflow
Duration
3 Days
Overview
Audience Profile
Prerequisities
At Course Completion
Course Outline
Overview
Audience Profile
Audience Profile
This course is designed for:
- Data scientists and machine learning engineers aiming to streamline their workflows.
- R developers interested in deploying and monitoring machine learning models.
- Professionals involved in machine learning operations (MLOps).
- Researchers and students focusing on the operational aspects of machine learning.
Prerequisities
Prerequisites
Participants should have:
- A computer with R and RStudio installed.
- Basic knowledge of R programming and machine learning concepts.
- Familiarity with version control systems (e.g., Git) and basic DevOps principles.
- Installation of necessary packages and libraries as guided in the course.
At Course Completion
At Course Completion
By the end of this course, participants will be able to:
- Understand the principles of MLOps and its significance in the machine learning lifecycle.
- Implement MLOps practices using R and MLflow for efficient model management.
- Automate the deployment and monitoring of machine learning models.
- Develop scalable and reproducible machine learning pipelines.
- Integrate MLflow with various tools and platforms for seamless workflow management.
Course Outline
Course Outline
Module 1: Introduction to MLOps and MLflow
- Introduction to MLOps
- Overview of MLOps and its importance in the ML lifecycle
- Key components and principles of MLOps
- Benefits and challenges of implementing MLOps
- Getting Started with MLflow
- Introduction to MLflow and its components
- Setting up the MLflow environment
- Tracking experiments with MLflow
- Basic Model Management with MLflow
- Logging parameters, metrics, and models
- Visualizing experiment results
- Organizing and searching experiments
Module 2: Advanced MLflow Usage and Model Deployment
- Advanced Features of MLflow
- Managing models in the MLflow Model Registry
- Versioning and promoting models to different stages
- Packaging and sharing MLflow projects
- Automating Model Deployment
- Introduction to automated deployment pipelines
- Deploying models as REST APIs using MLflow
- Integrating MLflow with Docker for containerization
- Monitoring and Managing Deployed Models
- Setting up monitoring for deployed models
- Retraining and updating models in production
- Best practices for maintaining model performance
Module 3: Integrating MLflow with Tools and Platforms
- Integration with Version Control and CI/CD
- Integrating MLflow with Git for version control
- Setting up continuous integration/continuous deployment (CI/CD) pipelines
- Automating workflows with GitHub Actions and MLflow
- Scalable Machine Learning Pipelines
- Building scalable ML pipelines with MLflow and R
- Orchestrating workflows with tools like Apache Airflow
- Case studies on scalable ML pipeline implementations
- Capstone Project and Best Practices
- Hands-on capstone project implementing MLOps practices
- Best practices for managing and scaling machine learning operations
- Future trends and advancements in MLOps
